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[付費5元查看完整內容]【CVPR 2022】一個完全無監督的框架，從噪聲和部分測量中學習圖像，Robust Equivariant Imaging: a fully unsupervised framework for learning to image

【一個完全無監督的框架，從噪聲和部分測量中學習圖像】Robust Equivariant Imaging: a fully unsupervised framework for learning to image from noisy and partial measurements
● 論文摘要：深度網絡在從醫學成像到計算攝影的多重成像逆問題中提供了最先進的性能。然而，大多數現有的網絡都是用干凈的信號訓練的，這通常很難或不可能獲得。等變成像(EI)是一種最新的自我監督學習框架，它利用信號分布中的群體不變性，僅從部分測量數據學習重構函數。雖然EI的結果令人印象深刻，但它的性能隨著噪聲的增加而下降。在本文中，我們提出了一個魯棒等變成像(REI)框架，它可以學習圖像從噪聲部分測量單獨。該方法使用Stein’s Unbiased Risk Estimator (SURE)來獲得對噪聲具有魯棒性的完全無監督訓練損失。我們表明，REI在線性和非線性逆問題上帶來了可觀的性能增益，從而為深度網絡的魯棒無監督成像鋪平了道路。
● 論文鏈接：//arxiv.org/abs/2111.12855
● 論文代碼：
● 作者單位：愛丁堡大學

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[付費5元查看完整內容]【CVPR 2022】盲圖像超分辨率退化分布的研究，Learning the Degradation Distribution for Blind Image Super-Resolution

Learning the Degradation Distribution for Blind Image Super-Resolution

Zhengxiong Luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan

當前的超分方法大多采用合成的成對的高清-低清樣本來訓練模型。為了避免合成數據與真實數據之間存在域差異，之前大部分方法采用可學習的退化模型去自適應地生成合成數據。這些降質模型通常是確定性的(deterministic)，即一張高清圖片只能用來合成一張低清樣本。然而，真實場景中的退化方法通常是隨機的，比如相機抖動造成的模糊和隨機噪聲。確定性的退化模型很難模擬真實退化方法的隨機性。針對這一問題，本文提出一種概率(probabilistic)退化模型。該模型把退化當作隨機變量進行研究，并通過學習從預定義的隨機變量到退化方法的映射來建模其分布。和以往的確定性退化模型相比，我們的概率退化模型可以模擬更加多樣的退化方法，從而生成更加豐富的高清-低清訓練樣本對，來幫助訓練更加魯棒的超分模型。在不同的數據集上的大量實驗表明，我們的方法可以幫助超分模型在復雜降質環境中取得更好的結果。

基于概率退化模型的盲超分模型結構圖

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Recent advances in deep learning theory

Deep learning is usually described as an experiment-driven field under continuous criticizes of lacking theoretical foundations. This problem has been partially fixed by a large volume of literature which has so far not been well organized. This paper reviews and organizes the recent advances in deep learning theory. The literature is categorized in six groups: (1) complexity and capacity-based approaches for analyzing the generalizability of deep learning; (2) stochastic differential equations and their dynamic systems for modelling stochastic gradient descent and its variants, which characterize the optimization and generalization of deep learning, partially inspired by Bayesian inference; (3) the geometrical structures of the loss landscape that drives the trajectories of the dynamic systems; (4) the roles of over-parameterization of deep neural networks from both positive and negative perspectives; (5) theoretical foundations of several special structures in network architectures; and (6) the increasingly intensive concerns in ethics and security and their relationships with generalizability.

[付費5元查看完整內容]【UIUC】最新《自監督學習》教程，51頁ppt，Self-supervised learning

來自UIUC最新《自監督學習》教程，

  • 數據預測
  • 彩色化
  • Transformation 預測
  • 上下文預測，拼圖游戲解決，旋轉預測
  • 深度聚類和實例預測
  • 對比學習
  • PIRL, MoCo, SimCLR, SWaV
  • 自我監督
  • 音頻、視頻、語言

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[付費5元查看完整內容]【CVPR2020】在線深度聚類的無監督表示學習, Online Deep Clustering for Unsupervised Representation Learning

題目： Online Deep Clustering for Unsupervised Representation Learning

摘要：

聯合聚類和特征學習方法在無監督表示學習中表現出了顯著的效果。但是，特征聚類和網絡參數更新訓練計劃的交替導致視覺表征學習的不穩定。為了克服這個挑戰，我們提出在線深度集群(ODC)，它可以同時執行集群和網絡更新，而不是交替進行。關鍵見解是，聚類中心應該穩步發展，以保持分類器的穩定更新。具體來說，設計和維護了兩個動態內存模塊，即樣本記憶用于存儲樣本標簽和特征，中心記憶用于中心進化。我們將全局聚類分解為穩定的內存更新和成批的標簽重新分配。該過程被集成到網絡更新迭代中。通過這種方式，標簽和網絡齊頭并進，而不是交替發展。大量的實驗表明，ODC能夠穩定訓練過程，有效地提高訓練性能。

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[付費5元查看完整內容]【CVPR2020】自監督的深度視覺測程與在線適應，Self-Supervised Deep Visual Odometry

自監督式VO方法在視頻中聯合估計攝像機姿態和深度方面取得了很大的成功。然而，與大多數數據驅動的方法一樣，現有的VO網絡在面對與訓練數據不同的場景時，性能顯著下降，不適合實際應用。在本文中，我們提出了一種在線元學習算法，使VO網絡能夠以一種自監督的方式不斷適應新的環境。該方法利用卷積長短時記憶(convLSTM)來聚合過去的豐富時空信息。網絡能夠記憶和學習過去的經驗，以便更好地估計和快速適應當前幀。在開放環境中運行VO時，為了應對環境的變化，我們提出了一種在線的特征對齊方法，即在不同的時刻對特征分布進行對齊。我們的VO網絡能夠無縫地適應不同的環境。在看不見的戶外場景、虛擬到真實世界和戶外到室內環境的大量實驗表明，我們的方法始終比最先進的自監督的VO基線性能更好。

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[付費5元查看完整內容]【CVPR2020-中科院計算所】弱監督語義分割的自監督等價注意力機制，Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

題目： Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation

摘要： 圖像級弱監督語義分割是近年來深入研究的一個具有挑戰性的問題。大多數高級解決方案都利用類激活映射(CAM)。然而，由于監督的充分性和弱監督的差距，CAMs很難作為目標掩模。在這篇論文中，我們提出了一個自我監督的等變注意機制(SEAM)來發現額外的監督并縮小差距。我們的方法是基于等方差是完全監督語義分割的一個隱含約束，其像素級標簽在數據擴充過程中與輸入圖像進行相同的空間變換。然而，這種約束在圖像級監控訓練的凸輪上丟失了。因此，我們提出了對不同變換圖像的預測凸輪進行一致性正則化，為網絡學習提供自監督。此外，我們提出了一個像素相關模塊(PCM)，它利用上下文外觀信息，并改進當前像素的預測由其相似的鄰居，從而進一步提高CAMs的一致性。在PASCAL VOC 2012數據集上進行的大量實驗表明，我們的方法在同等監督水平下表現優于最先進的方法。

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[付費5元查看完整內容]100+篇《自監督學習(Self-Supervised Learning)》論文最新合集

自監督學習(Self-Supervised Learning)是一種介于無監督和監督學習之間的一種新范式，旨在減少對大量帶注釋數據的挑戰性需求。它通過定義無注釋(annotation-free)的前置任務(pretext task)，為特征學習提供代理監督信號。jason718整理了關于自監督學習最新的論文合集，非常值得查看！

地址： //github.com/jason718/awesome-self-supervised-learning

A curated list of awesome Self-Supervised Learning resources. Inspired by , , , and

Why Self-Supervised?

Self-Supervised Learning has become an exciting direction in AI community.

  • Jitendra Malik: "Supervision is the opium of the AI researcher"
  • Alyosha Efros: "The AI revolution will not be supervised"
  • Yann LeCun: "self-supervised learning is the cake, supervised learning is the icing on the cake, reinforcement learning is the cherry on the cake"

Contributing

We Need You!

Please help contribute this list by contacting or add

Markdown format:

- Paper Name. 
  [[pdf]](link) 
  [[code]](link)
  - Author 1, Author 2, and Author 3. *Conference Year*

Table of Contents

Computer Vision

Survey

  • Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey.
    • Longlong Jing and Yingli Tian.

Image Representation Learning

Benchmark code

FAIR Self-Supervision Benchmark : various benchmark (and legacy) tasks for evaluating quality of visual representations learned by various self-supervision approaches.

2015

  • Unsupervised Visual Representation Learning by Context Prediction.

    • Doersch, Carl and Gupta, Abhinav and Efros, Alexei A. ICCV 2015

  • Unsupervised Learning of Visual Representations using Videos.

    • Wang, Xiaolong and Gupta, Abhinav. ICCV 2015

  • Learning to See by Moving.

    • Agrawal, Pulkit and Carreira, Joao and Malik, Jitendra. ICCV 2015

  • Learning image representations tied to ego-motion.

    • Jayaraman, Dinesh and Grauman, Kristen. ICCV 2015

2016

  • Joint Unsupervised Learning of Deep Representations and Image Clusters.

    • Jianwei Yang, Devi Parikh, Dhruv Batra. CVPR 2016

  • Unsupervised Deep Embedding for Clustering Analysis.

    • Junyuan Xie, Ross Girshick, and Ali Farhadi. ICML 2016

  • Slow and steady feature analysis: higher order temporal coherence in video.

    • Jayaraman, Dinesh and Grauman, Kristen. CVPR 2016

  • Context Encoders: Feature Learning by Inpainting.

    • Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A. CVPR 2016

  • Colorful Image Colorization.

    • Zhang, Richard and Isola, Phillip and Efros, Alexei A. ECCV 2016

  • Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles.

    • Noroozi, Mehdi and Favaro, Paolo. ECCV 2016

  • Ambient Sound Provides Supervision for Visual Learning.

    • Owens, Andrew and Wu, Jiajun and McDermott, Josh and Freeman, William and Torralba, Antonio. ECCV 2016

  • Learning Representations for Automatic Colorization.

    • Larsson, Gustav and Maire, Michael and Shakhnarovich, Gregory. ECCV 2016

  • Unsupervised Visual Representation Learning by Graph-based Consistent Constraints.

    • Li, Dong and Hung, Wei-Chih and Huang, Jia-Bin and Wang, Shengjin and Ahuja, Narendra and Yang, Ming-Hsuan. ECCV 2016

2017

  • Adversarial Feature Learning.

    • Donahue, Jeff and Krahenbuhl, Philipp and Darrell, Trevor. ICLR 2017

  • Self-supervised learning of visual features through embedding images into text topic spaces.

    • L. Gomez* and Y. Patel* and M. Rusi?ol and D. Karatzas and C.V. Jawahar. CVPR 2017

  • Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction.

    • Zhang, Richard and Isola, Phillip and Efros, Alexei A. CVPR 2017

  • Learning Features by Watching Objects Move.

    • Pathak, Deepak and Girshick, Ross and Dollar, Piotr and Darrell, Trevor and Hariharan, Bharath. CVPR 2017

  • Colorization as a Proxy Task for Visual Understanding.

    • Larsson, Gustav and Maire, Michael and Shakhnarovich, Gregory. CVPR 2017

  • DeepPermNet: Visual Permutation Learning.

    • Cruz, Rodrigo Santa and Fernando, Basura and Cherian, Anoop and Gould, Stephen. CVPR 2017

  • Unsupervised Learning by Predicting Noise.

    • Bojanowski, Piotr and Joulin, Armand. ICML 2017

  • Multi-task Self-Supervised Visual Learning.

    • Doersch, Carl and Zisserman, Andrew. ICCV 2017

  • Representation Learning by Learning to Count.

    • Noroozi, Mehdi and Pirsiavash, Hamed and Favaro, Paolo. ICCV 2017

  • Transitive Invariance for Self-supervised Visual Representation Learning.

    • Wang, Xiaolong and He, Kaiming and Gupta, Abhinav. ICCV 2017

  • Look, Listen and Learn.

    • Relja, Arandjelovic and Zisserman, Andrew. ICCV 2017

  • Unsupervised Representation Learning by Sorting Sequences.

    • Hsin-Ying Lee, Jia-Bin Huang, Maneesh Kumar Singh, and Ming-Hsuan Yang. ICCV 2017

2018

  • Unsupervised Feature Learning via Non-parameteric Instance Discrimination

    • Zhirong Wu, Yuanjun Xiong and X Yu Stella and Dahua Lin. CVPR 2018

  • Learning Image Representations by Completing Damaged Jigsaw Puzzles.

    • Kim, Dahun and Cho, Donghyeon and Yoo, Donggeun and Kweon, In So. WACV 2018

  • Unsupervised Representation Learning by Predicting Image Rotations.

    • Spyros Gidaris and Praveer Singh and Nikos Komodakis. ICLR 2018

  • Learning Latent Representations in Neural Networks for Clustering through Pseudo Supervision and Graph-based Activity Regularization.

    • Ozsel Kilinc and Ismail Uysal. ICLR 2018

  • Improvements to context based self-supervised learning.

    • Terrell Mundhenk and Daniel Ho and Barry Chen. CVPR 2018

  • Self-Supervised Feature Learning by Learning to Spot Artifacts.

    • Simon Jenni and Universit?t Bern and Paolo Favaro. CVPR 2018

  • Boosting Self-Supervised Learning via Knowledge Transfer.

    • Mehdi Noroozi and Ananth Vinjimoor and Paolo Favaro and Hamed Pirsiavash. CVPR 2018

  • Cross-domain Self-supervised Multi-task Feature Learning Using Synthetic Imagery.

    • Zhongzheng Ren and Yong Jae Lee. CVPR 2018

  • ShapeCodes: Self-Supervised Feature Learning by Lifting Views to Viewgrids.

    • Dinesh Jayaraman*, UC Berkeley; Ruohan Gao, University of Texas at Austin; Kristen Grauman. ECCV 2018

  • Deep Clustering for Unsupervised Learning of Visual Features

    • Mathilde Caron, Piotr Bojanowski, Armand Joulin, Matthijs Douze. ECCV 2018

  • Cross Pixel Optical-Flow Similarity for Self-Supervised Learning.

    • Aravindh Mahendran, James Thewlis, Andrea Vedaldi. ACCV 2018

2019

  • Representation Learning with Contrastive Predictive Coding.

    • Aaron van den Oord, Yazhe Li, Oriol Vinyals.

  • Self-Supervised Learning via Conditional Motion Propagation.

    • Xiaohang Zhan, Xingang Pan, Ziwei Liu, Dahua Lin, and Chen Change Loy. CVPR 2019

  • Self-Supervised Representation Learning by Rotation Feature Decoupling.

    • Zeyu Feng; Chang Xu; Dacheng Tao. CVPR 2019

  • Revisiting Self-Supervised Visual Representation Learning.

    • Alexander Kolesnikov; Xiaohua Zhai; Lucas Beye. CVPR 2019

  • AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Data.

    • Liheng Zhang, Guo-Jun Qi, Liqiang Wang, Jiebo Luo. CVPR 2019

  • Unsupervised Deep Learning by Neighbourhood Discovery. . .

    • Jiabo Huang, Qi Dong, Shaogang Gong, Xiatian Zhu. ICML 2019

  • Contrastive Multiview Coding.

    • Yonglong Tian and Dilip Krishnan and Phillip Isola.

  • Large Scale Adversarial Representation Learning.

    • Jeff Donahue, Karen Simonyan.

  • Learning Representations by Maximizing Mutual Information Across Views.

    • Philip Bachman, R Devon Hjelm, William Buchwalter

  • Selfie: Self-supervised Pretraining for Image Embedding.

    • Trieu H. Trinh, Minh-Thang Luong, Quoc V. Le

  • Data-Efficient Image Recognition with Contrastive Predictive Coding

    • Olivier J. He ?naff, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aaron van den Oord

  • Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty

    • Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, Dawn Song. NeurIPS 2019

  • Boosting Few-Shot Visual Learning with Self-Supervision

    • pyros Gidaris, Andrei Bursuc, Nikos Komodakis, Patrick Pérez, and Matthieu Cord. ICCV 2019

  • Self-Supervised Generalisation with Meta Auxiliary Learning

    • Shikun Liu, Andrew J. Davison, Edward Johns. NeurIPS 2019

  • Wasserstein Dependency Measure for Representation Learning

    • Sherjil Ozair, Corey Lynch, Yoshua Bengio, Aaron van den Oord, Sergey Levine, Pierre Sermanet. NeurIPS 2019

  • Scaling and Benchmarking Self-Supervised Visual Representation Learning

    • Priya Goyal, Dhruv Mahajan, Abhinav Gupta, Ishan Misra. ICCV 2019

2020

  • A critical analysis of self-supervision, or what we can learn from a single image

    • Yuki M. Asano, Christian Rupprecht, Andrea Vedaldi. ICLR 2020

  • On Mutual Information Maximization for Representation Learning

    • Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, Mario Lucic. ICLR 2020

  • Understanding the Limitations of Variational Mutual Information Estimators

    • Jiaming Song, Stefano Ermon. ICLR 2020

  • Automatic Shortcut Removal for Self-Supervised Representation Learning

    • Matthias Minderer, Olivier Bachem, Neil Houlsby, Michael Tschannen

  • Momentum Contrast for Unsupervised Visual Representation Learning

    • Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross Girshick. FAIR

  • A Simple Framework for Contrastive Learning of Visual Representations

    • Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton

  • ClusterFit: Improving Generalization of Visual Representations

    • Xueting Yan*, Ishan Misra*, Abhinav Gupta, Deepti Ghadiyaram**, Dhruv Mahajan**. CVPR 2020

  • Self-Supervised Learning of Pretext-Invariant Representations

    • Ishan Misra, Laurens van der Maaten. CVPR 2020

Video Representation Learning

  • Unsupervised Learning of Video Representations using LSTMs.

    • Srivastava, Nitish and Mansimov, Elman and Salakhudinov, Ruslan. ICML 2015

  • Shuffle and Learn: Unsupervised Learning using Temporal Order Verification.

    • Ishan Misra, C. Lawrence Zitnick and Martial Hebert. ECCV 2016

  • LSTM Self-Supervision for Detailed Behavior Analysis

    • Biagio Brattoli*, Uta Büchler*, Anna-Sophia Wahl, Martin E. Schwab, and Bj?rn Ommer. CVPR 2017

  • Self-Supervised Video Representation Learning With Odd-One-Out Networks.

    • Basura Fernando and Hakan Bilen and Efstratios Gavves and Stephen Gould. CVPR 2017

  • Unsupervised Learning of Long-Term Motion Dynamics for Videos.

    • Luo, Zelun and Peng, Boya and Huang, De-An and Alahi, Alexandre and Fei-Fei, Li. CVPR 2017

  • Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning.

    • Chuang Gan and Boqing Gong and Kun Liu and Hao Su and Leonidas J. Guibas. CVPR 2018

  • Improving Spatiotemporal Self-Supervision by Deep Reinforcement Learning.

    • Biagio Brattoli*, Uta Büchler*, and Bj?rn Ommer. ECCV 2018

  • Self-supervised learning of a facial attribute embedding from video.

    • Wiles, O., Koepke, A.S., Zisserman, A. BMVC 2018

  • Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles.

    • Kim, Dahun and Cho, Donghyeon and Yoo, Donggeun and Kweon, In So. AAAI 2019

  • Self-Supervised Spatio-Temporal Representation Learning for Videos by Predicting Motion and Appearance Statistics.

    • Jiangliu Wang; Jianbo Jiao; Linchao Bao; Shengfeng He; Yunhui Liu; Wei Liu. CVPR 2019

  • DynamoNet: Dynamic Action and Motion Network.

    • Ali Diba; Vivek Sharma, Luc Van Gool, Rainer Stiefelhagen. ICCV 2019

  • Learning Correspondence from the Cycle-consistency of Time.

    • Xiaolong Wang*, Allan Jabri* and Alexei A. Efros. CVPR 2019

  • Joint-task Self-supervised Learning for Temporal Correspondence.

    • Xueting Li*, Sifei Liu*, Shalini De Mello, Xiaolong Wang, Jan Kautz, and Ming-Hsuan Yang. NIPS 2019

Geometry

  • Self-supervised Learning of Motion Capture.

    • Tung, Hsiao-Yu and Tung, Hsiao-Wei and Yumer, Ersin and Fragkiadaki, Katerina. NIPS 2017

  • Unsupervised Learning of Depth and Ego-Motion from Video.

    • Zhou, Tinghui and Brown, Matthew and Snavely, Noah and Lowe, David G. CVPR 2017

  • Active Stereo Net: End-to-End Self-Supervised Learning for Active Stereo Systems.

    • Yinda Zhang*, Sean Fanello, Sameh Khamis, Christoph Rhemann, Julien Valentin, Adarsh Kowdle, Vladimir Tankovich, Shahram Izadi, Thomas Funkhouser. ECCV 2018

  • Self-Supervised Relative Depth Learning for Urban Scene Understanding.

    • Huaizu Jiang*, Erik Learned-Miller, Gustav Larsson, Michael Maire, Greg Shakhnarovich. ECCV 2018

  • Geometry-Aware Learning of Maps for Camera Localization.

    • Samarth Brahmbhatt, Jinwei Gu, Kihwan Kim, James Hays, and Jan Kautz. CVPR 2018

  • Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection.

    • David Novotny, Samuel Albanie, Diane Larlus, Andrea Vedaldi. CVPR 2018

  • Self-Supervised Learning of 3D Human Pose Using Multi-View Geometry.

    • Muhammed Kocabas; Salih Karagoz; Emre Akbas. CVPR 2019

  • SelFlow: Self-Supervised Learning of Optical Flow.

    • Jiangliu Wang; Jianbo Jiao; Linchao Bao; Shengfeng He; Yunhui Liu; Wei Liu. CVPR 2019

  • Unsupervised Learning of Landmarks by Descriptor Vector Exchange.

    • James Thewlis, Samuel Albanie, Hakan Bilen, Andrea Vedaldi. ICCV 2019

Audio

  • Audio-Visual Scene Analysis with Self-Supervised Multisensory Features.

    • Andrew Owens, Alexei A. Efros. ECCV 2018

  • Objects that Sound.

    • R. Arandjelovi?, A. Zisserman. ECCV 2018

  • Learning to Separate Object Sounds by Watching Unlabeled Video.

    • Ruohan Gao, Rogerio Feris, Kristen Grauman. ECCV 2018

  • The Sound of Pixels.

    • Zhao, Hang and Gan, Chuang and Rouditchenko, Andrew and Vondrick, Carl and McDermott, Josh and Torralba, Antonio. ECCV 2018

  • Learnable PINs: Cross-Modal Embeddings for Person Identity.

    • Arsha Nagrani, Samuel Albanie, Andrew Zisserman. ECCV 2018

  • Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization.

    • Bruno Korbar,Dartmouth College, Du Tran, Lorenzo Torresani. NIPS 2018

  • Self-Supervised Generation of Spatial Audio for 360° Video.

    • Pedro Morgado, Nuno Nvasconcelos, Timothy Langlois, Oliver Wang. NIPS 2018

  • TriCycle: Audio Representation Learning from Sensor Network Data Using Self-Supervision

    • Mark Cartwright, Jason Cramer, Justin Salamon, Juan Pablo Bello. WASPAA 2019

Others

  • Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model.
    • Qixiang Ye, Tianliang Zhang, Qiang Qiu, Baochang Zhang, Jie Chen, Guillermo Sapiro. CVPR 2017
  • Free Supervision from Video Games.
    • Philipp Kr?henbühl. CVPR 2018
  • Fighting Fake News: Image Splice Detection via Learned Self-Consistency
    • Minyoung Huh*, Andrew Liu*, Andrew Owens, Alexei A. Efros. ECCV 2018
  • Self-supervised Tracking by Colorization (Tracking Emerges by Colorizing Videos).
    • Carl Vondrick*, Abhinav Shrivastava, Alireza Fathi, Sergio Guadarrama, Kevin Murphy. ECCV 2018
  • High-Fidelity Image Generation With Fewer Labels.
    • Mario Lucic*, Michael Tschannen*, Marvin Ritter*, Xiaohua Zhai, Olivier Bachem, Sylvain Gelly.
  • Self-supervised Fitting of Articulated Meshes to Point Clouds.
    • Chun-Liang Li, Tomas Simon, Jason Saragih, Barnabás Póczos and Yaser Sheikh. CVPR 2019
  • SCOPS: Self-Supervised Co-Part Segmentation.
    • Wei-Chih Hung, Varun Jampani, Sifei Liu, Pavlo Molchanov, Ming-Hsuan Yang, and Jan Kautz. CVPR 2019
  • Self-Supervised GANs via Auxiliary Rotation Loss.
    • Ting Chen; Xiaohua Zhai; Marvin Ritter; Mario Lucic; Neil Houlsby. CVPR 2019
  • Self-Supervised Adaptation of High-Fidelity Face Models for Monocular Performance Tracking.
    • Jae Shin Yoon; Takaaki Shiratori; Shoou-I Yu; Hyun Soo Park. CVPR 2019
  • Multi-Task Self-Supervised Object Detection via Recycling of Bounding Box Annotations.
    • Wonhee Lee; Joonil Na; Gunhee Kim. CVPR 2019
  • Self-Supervised Convolutional Subspace Clustering Network.
    • Junjian Zhang; Chun-Guang Li; Chong You; Xianbiao Qi; Honggang Zhang; Jun Guo; Zhouchen Lin. CVPR 2019
  • Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation.
    • Xin Wang; Qiuyuan Huang; Asli Celikyilmaz; Jianfeng Gao; Dinghan Shen; Yuan-Fang Wang; William Yang Wang; Lei Zhang. CVPR 2019
  • Unsupervised 3D Pose Estimation With Geometric Self-Supervision.
    • Ching-Hang Chen; Ambrish Tyagi; Amit Agrawal; Dylan Drover; Rohith MV; Stefan Stojanov; James M. Rehg. CVPR 2019
  • Learning to Generate Grounded Image Captions without Localization Supervision.
    • Chih-Yao Ma; Yannis Kalantidis; Ghassan AlRegib; Peter Vajda; Marcus Rohrbach; Zsolt Kira.
  • VideoBERT: A Joint Model for Video and Language Representation Learning
    • Chen Sun, Austin Myers, Carl Vondrick, Kevin Murphy, Cordelia Schmid. ICCV 2019
  • S4L: Self-Supervised Semi-Supervised Learning
    • Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, Lucas Beyer
  • Countering Noisy Labels By Learning From Auxiliary Clean Labels
    • Tsung Wei Tsai, Chongxuan Li, Jun Zhu

Machine Learning

  • Self-taught Learning: Transfer Learning from Unlabeled Data.

    • Raina, Rajat and Battle, Alexis and Lee, Honglak and Packer, Benjamin and Ng, Andrew Y. ICML 2007

  • Representation Learning: A Review and New Perspectives.

    • Bengio, Yoshua and Courville, Aaron and Vincent, Pascal. TPAMI 2013.

Reinforcement Learning

  • Curiosity-driven Exploration by Self-supervised Prediction.

    • Deepak Pathak, Pulkit Agrawal, Alexei A. Efros, and Trevor Darrell. ICML 2017

  • Large-Scale Study of Curiosity-Driven Learning.

    • Yuri Burda*, Harri Edwards*, Deepak Pathak*, Amos Storkey, Trevor Darrell and Alexei A. Efros

  • Playing hard exploration games by watching YouTube.

    • Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas. NIPS 2018

  • Unsupervised State Representation Learning in Atari.

    • Ankesh Anand, Evan Racah, Sherjil Ozair, Yoshua Bengio, Marc-Alexandre C?té, R Devon Hjelm. NeurIPS 2019

Robotics

2006

  • Improving Robot Navigation Through Self-Supervised Online Learning

    • Boris Sofman, Ellie Lin, J. Andrew Bagnell, Nicolas Vandapel, and Anthony Stentz

  • Reverse Optical Flow for Self-Supervised Adaptive Autonomous Robot Navigation

    • A. Lookingbill, D. Lieb, J. Rogers and J. Curry

2009

  • Learning Long-Range Vision for Autonomous Off-Road Driving
    • Raia Hadsell, Pierre Sermanet, Jan Ben, Ayse Erkan, Marco Scoffier, Koray Kavukcuoglu, Urs Muller, Yann LeCun

2012

  • Self-supervised terrain classification for planetary surface exploration rovers
    • Christopher A. Brooks, Karl Iagnemma

2014

  • Terrain Traversability Analysis Using Multi-Sensor Data Correlation by a Mobile Robot
    • Mohammed Abdessamad Bekhti, Yuichi Kobayashi and Kazuki Matsumura

2015

  • Online self-supervised learning for dynamic object segmentation

    • Vitor Guizilini and Fabio Ramos, The International Journal of Robotics Research

  • Self-Supervised Online Learning of Basic Object Push Affordances

    • Barry Ridge, Ales Leonardis, Ales Ude, Miha Denisa, and Danijel Skocaj

  • Self-supervised learning of grasp dependent tool affordances on the iCub Humanoid robot

    • Tanis Mar, Vadim Tikhanoff, Giorgio Metta, and Lorenzo Natale

2016

  • Persistent self-supervised learning principle: from stereo to monocular vision for obstacle avoidance

    • Kevin van Hecke, Guido de Croon, Laurens van der Maaten, Daniel Hennes, and Dario Izzo

  • The Curious Robot: Learning Visual Representations via Physical Interactions.

    • Lerrel Pinto and Dhiraj Gandhi and Yuanfeng Han and Yong-Lae Park and Abhinav Gupta. ECCV 2016

  • Learning to Poke by Poking: Experiential Learning of Intuitive Physics.

    • Agrawal, Pulkit and Nair, Ashvin V and Abbeel, Pieter and Malik, Jitendra and Levine, Sergey. NIPS 2016

  • Supersizing Self-supervision: Learning to Grasp from 50K Tries and 700 Robot Hours.

    • Pinto, Lerrel and Gupta, Abhinav. ICRA 2016

2017

  • Supervision via Competition: Robot Adversaries for Learning Tasks.

    • Pinto, Lerrel and Davidson, James and Gupta, Abhinav. ICRA 2017

  • Multi-view Self-supervised Deep Learning for 6D Pose Estimation in the Amazon Picking Challenge.

    • Andy Zeng, Kuan-Ting Yu, Shuran Song, Daniel Suo, Ed Walker Jr., Alberto Rodriguez, Jianxiong Xiao. ICRA 2017

  • Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation.

    • Ashvin Nair*, Dian Chen*, Pulkit Agrawal*, Phillip Isola, Pieter Abbeel, Jitendra Malik, Sergey Levine. ICRA 2017

  • Learning to Fly by Crashing

    • Dhiraj Gandhi, Lerrel Pinto, Abhinav Gupta IROS 2017

  • Self-supervised learning as an enabling technology for future space exploration robots: ISS experiments on monocular distance learning

    • K. van Hecke, G. C. de Croon, D. Hennes, T. P. Setterfield, A. Saenz- Otero, and D. Izzo

  • Unsupervised Perceptual Rewards for Imitation Learning.

    • Sermanet, Pierre and Xu, Kelvin and Levine, Sergey. RSS 2017

  • Self-Supervised Visual Planning with Temporal Skip Connections.

    • Frederik Ebert, Chelsea Finn, Alex X. Lee, Sergey Levine. CoRL2017

2018

  • CASSL: Curriculum Accelerated Self-Supervised Learning.

    • Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, Abhinav Gupta. ICRA 2018

  • Time-Contrastive Networks: Self-Supervised Learning from Video.

    • Pierre Sermanet and Corey Lynch and Yevgen Chebotar and Jasmine Hsu and Eric Jang and Stefan Schaal and Sergey Levine. ICRA 2018

  • Self-Supervised Deep Reinforcement Learning with Generalized Computation Graphs for Robot Navigation.

    • Gregory Kahn, Adam Villaflor, Bosen Ding, Pieter Abbeel, Sergey Levine. ICRA 2018

  • Learning Actionable Representations from Visual Observations.

    • Dwibedi, Debidatta and Tompson, Jonathan and Lynch, Corey and Sermanet, Pierre. IROS 2018

  • Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning.

    • Andy Zeng, Shuran Song, Stefan Welker, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser. IROS 2018

  • Visual Reinforcement Learning with Imagined Goals.

    • Ashvin Nair*, Vitchyr Pong*, Murtaza Dalal, Shikhar Bahl, Steven Lin, Sergey Levine.NeurIPS 2018

  • Grasp2Vec: Learning Object Representations from Self-Supervised Grasping.

    • Eric Jang*, Coline Devin*, Vincent Vanhoucke, Sergey Levine. CoRL 2018

  • Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning.

    • Frederik Ebert, Sudeep Dasari, Alex X. Lee, Sergey Levine, Chelsea Finn. CoRL 2018

2019

  • Learning Long-Range Perception Using Self-Supervision from Short-Range Sensors and Odometry.

    • Mirko Nava, Jerome Guzzi, R. Omar Chavez-Garcia, Luca M. Gambardella, Alessandro Giusti. Robotics and Automation Letters

  • Learning Latent Plans from Play.

    • Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet

2020

  • Adversarial Skill Networks: Unsupervised Robot Skill Learning from Video.
    • Oier Mees, Markus Merklinger, Gabriel Kalweit, Wolfram Burgard ICRA 2020

NLP

  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

    • Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. NAACL 2019 Best Long Paper

  • Self-Supervised Dialogue Learning

    • Jiawei Wu, Xin Wang, William Yang Wang. ACL 2019

  • Self-Supervised Learning for Contextualized Extractive Summarization

    • Hong Wang, Xin Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang. ACL 2019

  • A Mutual Information Maximization Perspective of Language Representation Learning

    • Lingpeng Kong, Cyprien de Masson d'Autume, Lei Yu, Wang Ling, Zihang Dai, Dani Yogatama. ICLR 2020

  • VL-BERT: Pre-training of Generic Visual-Linguistic Representations

    • Weijie Su, Xizhou Zhu, Yue Cao, Bin Li, Lewei Lu, Furu Wei, Jifeng Dai. ICLR 2020

ASR

  • Learning Robust and Multilingual Speech Representations

    • Kazuya Kawakami, Luyu Wang, Chris Dyer, Phil Blunsom, Aaron van den Oord

  • Unsupervised pretraining transfers well across languages

    • Morgane Riviere, Armand Joulin, Pierre-Emmanuel Mazare, Emmanuel Dupoux

  • wav2vec: Unsupervised Pre-Training for Speech Recognition

    • Steffen Schneider, Alexei Baevski, Ronan Collobert, Michael Auli. INTERSPEECH 2019

  • vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

    • Alexei Baevski, Steffen Schneider, Michael Auli. ICLR 2020

  • Effectiveness of self-supervised pre-training for speech recognition

    • Alexei Baevski, Michael Auli, Abdelrahman Mohamed

  • Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learning

    • Alexander H. Liu, Tao Tu, Hung-yi Lee, Lin-shan Lee

  • Self-Training for End-to-End Speech Recognition

    • Jacob Kahn, Ann Lee, Awni Hannun. ICASSP 2020

  • Generative Pre-Training for Speech with Autoregressive Predictive Coding

    • Yu-An Chung, James Glass. ICASSP 2020

Talks

  • The power of Self-Learning Systems. Demis Hassabis (DeepMind).
  • Supersizing Self-Supervision: Learning Perception and Action without Human Supervision. Abhinav Gupta (CMU).
  • Self-supervision, Meta-supervision, Curiosity: Making Computers Study Harder. Alyosha Efros (UCB)
  • Unsupervised Visual Learning Tutorial. CVPR 2018
  • Self-Supervised Learning. Andrew Zisserman (Oxford & Deepmind).
  • Graph Embeddings, Content Understanding, & Self-Supervised Learning. Yann LeCun. (NYU & FAIR)
  • Self-supervised learning: could machines learn like humans? Yann LeCun @EPFL.
  • Week 9 (b): CS294-158 Deep Unsupervised Learning(Spring 2019). Alyosha Efros @UC Berkeley.

Thesis

  • Supervision Beyond Manual Annotations for Learning Visual Representations. Carl Doersch. .
  • Image Synthesis for Self-Supervised Visual Representation Learning. Richard Zhang. .
  • Visual Learning beyond Direct Supervision. Tinghui Zhou. .
  • Visual Learning with Minimal Human Supervision. Ishan Misra. .

Blog

  • Self-Supervised Representation Learning. Lilian Weng. .
  • The Illustrated Self-Supervised Learning. Amit Chaudhary.

License

To the extent possible under law, has waived all copyright and related or neighboring rights to this work.

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[付費5元查看完整內容]【超分辨率| 2019最新綜述】圖像超分辨率的深度學習，附PDF（Deep Learning for Image Super-resolution: A Survey）

論文主題： Deep Learning for Image Super-resolution: A Survey

論文摘要： 圖像超分辨率（SR）是提高圖像分辨率的一類重要的圖像處理技術以及計算機視覺中的視頻。近年來，基于深度學習的圖像超分辨率研究取得了顯著進展技術。在這項調查中，我們旨在介紹利用深度學習的圖像超分辨率技術的最新進展系統的方法。一般來說，我們可以粗略地將現有的SR技術研究分為三大類：監督SR、非監督SR和領域特定SR。此外，我們還討論了一些其他重要問題，如公開可用的基準數據集和性能評估指標。最后，我們通過強調幾個未來來結束這項調查未來社區應進一步解決的方向和公開問題.

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