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Paper with Code --- [NeurIPS2020]

Image Generation or Synthesis

  1. Few-shot Image Generation with Elastic Weight Consolidation, Yijun Li Richard Zhang Jingwan Lu Eli Shechtman, [Paper]
  2. Neural FFTs for Universal Texture Image Synthesis, Morteza Mardani, Guilin Liu, Aysegul Dundar, Shiqiu Liu, Andrew Tao, Bryan Catanzaro, [Paper]
  3. Fourier Spectrum Discrepancies in Deep Network Generated Images, Tarik Dzanic, Karan Shah, Freddie Witherden, [Paper]
  4. Swapping Autoencoder for Deep Image Manipulation, Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei Efros, Richard Zhang, [Paper], [Code]
  5. DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs, yaxing wang, Lu Yu, Joost van de Weijer, [Paper], [Code]
  6. ContraGAN: Contrastive Learning for Conditional Image Generation, Minguk Kang, Jaesik Park, [Paper]
  7. Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation, Bowen Li, Xiaojuan Qi, Philip Torr, Thomas Lukasiewicz, [Paper]
  8. Data-Efficient GANs with DiffAugment, Shengyu Zhao, Zhijian Liu, Ji Lin, Jun-Yan Zhu, Song Han, [Project], [Paper]

Image Restoration (Denosing, Super-Resolution, Deblurring)

  1. Unfolding the Alternating Optimization for Blind Super Resolution, zhengxiong luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan, [Paper], [Code]
  2. Cross-Scale Internal Graph Neural Network for Image Super-Resolution, Shangchen Zhou, Jiawei Zhang, Wangmeng Zuo, Chen Change Loy, [Paper], [Code]
  3. Neural Sparse Representation for Image Restoration, Yuchen Fan, Jiahui Yu, Yiqun Mei, Yulun Zhang, Yun Fu, Ding Liu, Thomas S. Huang, [Paper], [Code]
  4. CLEARER: Multi-Scale Neural Architecture Search for Image Restoration, Yuanbiao Gou, Boyun Li, Zitao Liu, Songfan Yang, Xi Peng, [Paper]
  5. Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising, Yaochen Xie, Zhengyang Wang, Shuiwang Ji, [Paper], [Code]
  6. LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond, Wenbo Li, Kun Zhou, Lu Qi, Nianjuan Jiang, Jiangbo Lu, Jiaya Jia, [Paper], [Code]
  7. Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring, Jiangxin Dong, Stefan Roth, Bernt Schiele, [Paper], [Code]

HDR

  1. UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging, Chu Zhou, Hang Zhao, Jin Han, Chang Xu, Chao Xu, Tiejun Huang, Boxin Shi, [Paper], [Code]

Demoiré

  1. Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs， Lin Liu, Shanxin Yuan, Jianzhuang Liu, Liping Bao, Gregory Slabaugh, Qi Tian, [Paper]

Video Interpolation

  1. Blind Video Temporal Consistency via Deep Video Prior, Chenyang Lei, Yazhou Xing, Qifeng Chen, [Paper], [Code]
  2. Video Frame Interpolation without Temporal Priors, Youjian Zhang, Chaoyue Wang, Dacheng Tao, [Paper], [Code]

Continual Learning

  1. Continual Learning with Node-Importance based Adaptive Group Sparse Regularization, Sangwon Jung, Hongjoon Ahn, Sungmin Cha, Taesup Moon, [Paper], [Code]
  2. Continual Deep Learning by Functional Regularisation of Memorable Past, Pingbo Pan, Siddharth Swaroop, Alexander Immer, Runa Eschenhagen, Richard Turner, Mohammad Emtiyaz E. Khan, [Paper], [Code]
  3. Continual Learning of Control Primitives : Skill Discovery via Reset-Games, Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine, [Paper], [code]
  4. Understanding the Role of Training Regimes in Continual Learning, Seyed Iman Mirzadeh, Mehrdad Farajtabar, Razvan Pascanu, Hassan Ghasemzadeh, [Paper], [Code]
  5. Continual Learning in Low-rank Orthogonal Subspaces, Arslan Chaudhry, Naeemullah Khan, Puneet Dokania, Philip Torr, [Paper], [Code]
  6. Look-ahead Meta Learning for Continual Learning, Gunshi Gupta, Karmesh Yadav, Liam Paull, [Paper], [Code]
  7. Meta-Consolidation for Continual Learning, oseph K J, Vineeth Nallure Balasubramanian, [Paper], [Code]
  8. Dark Experience for General Continual Learning: a Strong, Simple Baseline, Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, SIMONE CALDERARA, [Paper], [Code]
  9. Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning, Massimo Caccia, Pau Rodriguez, Oleksiy Ostapenko, Fabrice Normandin, Min Lin, Lucas Page-Caccia, Issam Hadj Laradji, Irina Rish, Alexandre Lacoste, David Vázquez, Laurent Charlin, [Paper], [Code]
  10. Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization, Hung-Jen Chen, An-Chieh Cheng, Da-Cheng Juan, Wei Wei, Min Sun, [Paper], [Code]
  11. Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks, Zixuan Ke, Bing Liu, Xingchang Huang, [Paper], [Code]
  1. Rethinking Pre-training and Self-training, Barret Zoph, Golnaz Ghiasi, Tsung-Yi Lin, Yin Cui, Hanxiao Liu, Ekin Dogus Cubuk, Quoc Le, [Paper], [Code]
  2. Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation, Guoliang Kang, Yunchao Wei, Yi Yang, Yueting Zhuang, Alexander Hauptmann, [Paper], [Code]
  3. Fast Fourier Convolution, Lu Chi, Borui Jiang, Yadong Mu, [Paper], [The pseudo-code is given in the article]

Tutorial

  1. Abstraction & Reasoning in AI systems: Modern Perspectives, [Project], [Video Part1], [Video Part2]