来源：兔子小姐@知乎
本文整理了CVPR2020关于元学习、小样本、领域自适应、领域泛化以及迁移学习的论文，没有链接的是还没有放出来的论文，有遗漏或错误欢迎指正。
- 元学习（meta-learning）
- 小样本（few-shot learning）
- 元学习与小样本/零样本结合
- 领域泛化（domain generalization）
-
迁移学习（transfer learning）
元学习（meta-learning）
Learning Meta Face Recognition in Unseen Domains
论文下载地址：https://arxiv.org/abs/2003.07733
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation
论文下载地址：https://arxiv.org/abs/1911.07450
Scene-Adaptive Video Frame Interpolation via Meta-Learning
论文下载地址：https://arxiv.org/abs/2004.00779
Training Noise-Robust Deep Neural Networks via Meta-Learning
论文下载地址：https://openaccess.thecvf.com/content CVPR 2020/html/Wang Training Noise-Robust Deep Neural Networks via Meta-Learning CVPR 2020 paper.html
Learning to Forget for Meta-Learning
论文下载地址：https://arxiv.org/abs/1906.05895
Tracking by Instance Detection：A Meta-Learning Approach
论文下载地址：https://openaccess.thecvf.com/content CVPR 2020/papers/Wang Tracking by I
MetaIQA Deep Meta-Learning for No-Reference Image Quality Assessment
论文下载地址：https://arxiv.org/abs/2004.05508
iTAML:An Incremental Task-Agnostic Meta-learning Approach
论文下载地址：https://arxiv.org/abs/2003.11652
小样本（few-shot learning）
FSS-1000:A 1000-Class Dataset for Few-Shot Segmentation
论文下载地址：https://arxiv.org/abs/1907.12347
3FabRec:Fast Few-Shot Face Alignment by Reconstruction
论文下载地址：https://arxiv.org/abs/1911.10448
DeepEMD:Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers
Few-Shot Class-Incremental Learning
论文下载地址：https://arxiv.org/abs/2004.10956
Few-Shot Pill Recognition
FGN:Fully Guided Network for Few-Shot Instance Segmentation
论文下载地址：https://arxiv.org/abs/2003.13954
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation
Learning to Select Base Classes for Few-Shot Classification
论文下载地址：https://arxiv.org/abs/2004.00315
Semi-Supervised Learning for Few-Shot Image-to-Image Translation
论文下载地址：https://arxiv.org/abs/2003.13853
CRNet:Cross-Reference Networks for Few-Shot Segmentation
论文下载地址：https://arxiv.org/abs/2003.10658
Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector
论文下载地址：https://arxiv.org/abs/1908.01998
Multi-Domain Learning for Accurate and Few-Shot Color Constancy
论文下载地址：http://www4.comp.polyu.edu.hk/\~cslzhang/paper/MDLCC.pdf
Few-Shot Learning via Embedding Adaptation With Set-to-Set Functions
论文下载地址：https://arxiv.org/abs/1812.03664
Adaptive Subspaces for Few-Shot Learning
Few-Shot Video Classification via Temporal Alignment
论文下载地址：https://arxiv.org/abs/1906.11415
Boosting Few-Shot Learning With Adaptive Margin Loss
论文下载地址：https://arxiv.org/abs/2005.13826
TransMatch:A Transfer-Learning Scheme for Semi-Supervised Few-Shot Learning
论文下载地址：https://arxiv.org/abs/1912.09033
Instance Credibility Inference for Few-Shot Learning
论文下载地址：https://arxiv.org/abs/2003.11853
DPGN:Distribution Propagation Graph Network for Few-Shot Learning
论文下载地址：https://arxiv.org/abs/2003.14247
Adversarial Feature Hallucination Networks for Few-Shot Learning
论文下载地址：https://arxiv.org/abs/2003.13193
Revisiting Pose-Normalization for Fine-Grained Few-Shot Recognition
论文下载地址：https://arxiv.org/abs/2004.00705
Improved Few-Shot Visual Classification
论文下载地址：https://arxiv.org/abs/1912.03432
元学习与小样本/零样本：
Meta-Transfer Learning for Zero-Shot Super-Resolution
论文下载地址：https://arxiv.org/abs/2002.12213
Few-Shot Open-Set Recognition Using Meta-Learning
论文下载地址：https://arxiv.org/abs/2005.13713
Meta-Learning of Neural Architectures for Few-Shot Learning
论文下载地址：https://arxiv.org/abs/1911.11090
领域自适应（domain adaptation）
Domain Adaptation for Image Dehazing
论文下载地址：https://arxiv.org/abs/2005.04668
Weakly-Supervised Domain Adaptation via GAN and Mesh Model for Estimating
One-Shot Domain Adaptation for Face Generation
论文下载地址：https://arxiv.org/abs/2003.12869
Model Adaptation Unsupervised Domain Adaptation Without Source Data
Progressive Adversarial Networks for Fine-Grained Domain Adaptation
论文下载地址：http://ise.thss.tsinghua.edu.cn/\~mlong/doc/progressive-adversarial-networks-cvpr20.pdf
Action Segmentation With Joint Self-Supervised Temporal Domain Adaptation
论文下载地址：https://arxiv.org/abs/2003.02824
Stochastic Classifiers for Unsupervised Domain Adaptation
Spherical Space Domain Adaptation With Robust Pseudo-Label Loss
Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation
Universal Source-Free Domain Adaptation
论文下载地址：https://arxiv.org/abs/2004.04393
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation
论文下载地址：https://arxiv.org/abs/2005.02066
Unsupervised Domain Adaptation With Hierarchical Gradient Synchronization
Phase Consistent Ecological Domain Adaptation
论文下载地址：https://arxiv.org/abs/2004.04923
What Can Be Transferred Unsupervised Domain Adaptation for Endoscopic Lesions
论文下载地址：https://arxiv.org/abs/2004.11500
FDA:Fourier Domain Adaptation for Semantic Segmentation
论文下载地址：https://arxiv.org/abs/2004.05498
Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering
论文下载地址：https://arxiv.org/abs/2003.08607
Multi-Modal Domain Adaptation for Fine-Grained Action Recognition
论文下载地址：https://arxiv.org/abs/2001.09691
Unsupervised Intra-Domain Adaptation for Semantic Segmentation Through Self-Supervision
论文下载地址：https://arxiv.org/abs/2004.07703
Gradually Vanishing Bridge for Adversarial Domain Adaptation
论文下载地址：https://arxiv.org/abs/2003.13183
Open Compound Domain Adaptation
论文下载地址：https://arxiv.org/abs/1909.03403
Selective Transfer With Reinforced Transfer Network for Partial Domain Adaptation
论文下载地址：https://arxiv.org/abs/1905.10756
xMUDA:Cross-Modal Unsupervised Domain Adaptation for 3D Semantic Segmentation
论文下载地址：https://arxiv.org/abs/1911.12676
Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation
论文下载地址：https://arxiv.org/abs/2003.00867
Towards Inheritable Models for Open-Set Domain Adaptation
论文下载地址：https://arxiv.org/abs/2004.04388
Disparity-Aware Domain Adaptation in Stereo Image Restoration
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation
论文下载地址：https://arxiv.org/abs/2003.10275
Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation
论文下载地址：https://arxiv.org/abs/2006.06567
Enhanced Transport Distance for Unsupervised Domain Adaptation
Light-weight Calibrator A Separable Component for Unsupervised Domain Adaptation
论文下载地址：https://arxiv.org/abs/1911.12796
领域泛化（domain generalization）
Single-Side Domain Generalization for Face Anti-Spoofing
论文下载地址：https://arxiv.org/abs/2004.14043
Learning to Learn Single Domain Generalization
论文下载地址：https://arxiv.org/abs/2003.13216
迁移学习（transfer learning）
Syn2Real Transfer Learning for Image Deraining Using Gaussian Processes
论文下载地址：https://arxiv.org/abs/2006.05580
Camera On-Boarding for Person Re-Identification Using Hypothesis Transfer Learning
论文下载地址：https://vcg.engr.ucr.edu/sites/g/files/rcwecm2661/files/2020-04/09517.pdf
Multi-Mutual Consistency Induced Transfer Subspace Learning for Human Motion Segmentation
LT-Net:Label Transfer by Learning Reversible Voxel-Wise Correspondence for One-Shot
论文下载地址：https://arxiv.org/abs/2003.07072
Transfer Learning From Synthetic to Real-Noise Denoising With Adaptive Instance
论文下载地址：https://arxiv.org/abs/2002.11244
Learning to Transfer Texture From Clothing Images to 3D Humans
论文下载地址：https://arxiv.org/abs/2003.02050
Neural Data Server A Large-Scale Search Engine for Transfer Learning
论文下载地址：https://arxiv.org/abs/2001.02799
Regularizing CNN Transfer Learning With Randomised Regression
论文下载地址：https://arxiv.org/abs/1908.05997
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CVPR 2020论文大盘点
小样本学习（Few-shot Learning）综述
机器学习领域最全综述列表！
从因果关系来看小样本学习丨 NeurIPS 2020
