This repository is SSL for Image Representation, one of the OpenLab's PseudoLab.

Introduce page
Every Monday at 10pm, PseudoLab Discord Room YL!

• Wongi Park | Github | LinkedIn |
• Jaehyeong Chun | Github | LinkedIn |
• Dongryeol Lee | Github | LinkedIn |
• Haemun Kim

• Survey and Analysis
• Contrastive & Distillation Learninig
• Masked Auto Encoder
• Image Transformation
• Vision Language Model
• Clustering
• Domain Generalization
• Anomaly Detection
• Few-shot learning
• Dataset
• Blog and Resource

• [ Analysis ] Unsupervised Deep Embedding for Clustering Analysis. (ICML 2016) [Paper] [CODE]
• [ Analysis ] Revisiting self-supervised visual representation learning (CVPR 2019) [Paper] [CODE]
• [ Analysis ] What Makes for Good Views for Contrastive Learning? (NIPS 2020) [Paper]
• [ Analysis ] A critical analysis of self-supervision, or what we can learn from a single image (ICLR 2020) [Paper]
• [ Analysis ] How Useful is Self-Supervised Pretraining for Visual Tasks? (CVPR 2020) [Paper] [CODE]
• [ Analysis ] How Well Do Self-Supervised Models Transfer? (CVPR 2021) [Paper]
• [ Analysis ] Understanding Dimensional Collapse in Contrastive Self-supervised Learning (ICLR 2022) [Paper]
• [ Analysis ] Revealing the Dark Secrets of Masked Image Modeling (CVPR 2023) [Paper]
• [ Analysis ] What do Self-Supervised Vision Transformers Learn? (ICLR 2023) [Paper]

• [ TraS ] Transitive Invariance for Self-supervised Visual Representation Learning. (ICCV 2017) [Paper]
• [ NonID ] Unsupervised Feature Learning via Non-parameteric Instance Discrimination. (CVPR 2018) [Paper] [CODE]
• [ MoCo ] Momentum Contrast for Unsupervised Visual Representation Learning (CVPR 2019) [Paper] [CODE]
• [ MoCoV2 ] Improved Baselines with Momentum Contrastive Learning (ArXiv 2020) [Paper] [CODE]
• [ SimCLR ] A Simple Framework for Contrastive Learning of Visual Representations (ICML 2020) [Paper] [CODE]
• [ SimCLRv2 ] Big Self-Supervised Models are Strong Semi-Supervised Learners (NIPS 2020) [Paper] [CODE]
• [ SwAV ] Unsupervised Learning of Visual Features by Contrasting Cluster Assignments (NIPS 2020) [Paper] [CODE]
• [ Reasoning ] Self-Supervised Relational Reasoning for Representation Learning (NIPS 2020) [Paper] [CODE]
• [ PIRL ] Self-Supervised Learning of Pretext-Invariant Representations (CVPR 2020) [Paper] [CODE]
• [ SEED ] SEED: Self-supervised Distillation For Visual Representation (ICLR 2021) [Paper] [CODE]
• [ SimSiam ] Exploring Simple Siamese Representation Learning. (CVPR 2021) [Paper] [CODE]
• [ PixPro ] Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning. (CVPR 2021) [Paper] [CODE]
• [ BYOL ] Bootstrap Your Own Latent A New Approach to Self-Supervised Learning (NIPS 2020) [Paper] [CODE]
• [ RoCo ] Robust Contrastive Learning Using Negative Samples with Diminished Semantics (NIPS 2021) [Paper] [CODE]
• [ ImCo ] Improving Contrastive Learning by Visualizing Feature Transformation (ICCV 2021) [Paper] [CODE]
• [ DINO ] Emerging Properties in Self-Supervised Vision Transformers (ICCV 2021) [Paper] [CODE]
• [ Barlow Twins ] Barlow Twins: Self-Supervised Learning via Redundancy Reduction (ICML 2021) [Paper] [CODE]
• [ VICReg ] VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning (ICLR 2022) [Paper] [CODE]
• [ E-SSL ] E-SSL: Equivariant Contrastive Learning (ICLR 2022) [Paper] [CODE]
• [ TriBYOL ] TriBYOL: Triplet BYOL for Self-Supervised Representation Learning (ICASSP 2022) [Paper]
• [ DINOv2 ] DINOv2: Learning Robust Visual Features without Supervision (ArXiv 2023) [Paper] [CODE]
• [ AVT ] AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformations (CVPR 2018) [Paper] [CODE]
• [ MoCHI ] MoCHI: Hard Negative Mixing for Contrastive Learning (NIPS 2020) [Paper] [CODE]
• [ SMDistill ] Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly (NIPS 2021) [Paper]
• [ BURN ] BURN: Unsupervised Representation Learning for Binary Networks by Joint Classifier Training (CVPR 2022) [Paper] [CODE]
• [ DenseCL ] Dense Contrastive Learning for Self-Supervised Visual Pre-Training (CVPR 2021) [Paper] [CODE]
• [ RINCE ] Robust Contrastive Learning against Noisy Views (CVPR 2021) [Paper] [CODE]
• [ SEED ] SEED: Self-supervised Distillation For Visual Representation (ICLR 2021) [Paper] [CODE]

• [ MAE ] Masked Autoencoders Are Scalable Vision Learners (CVPR 2020) [Paper] [CODE]
• [ MST ] MST: Masked Self-Supervised Transformer for Visual Representation (NIPS 2021) [Paper]
• [ SimMiM ] SimMIM: A Simple Framework for Masked Image Modeling (CVPR 2021) [Paper] [CODE]
• [ Adios ] Adversarial Masking for Self-Supervised Learning (ICML 2022) [Paper] [CODE]
• [ iBOT ] iBOT 🤖: Image BERT Pre-Training with Online Tokenizer (ICLR 2022) [Paper] [CODE]
• [ BEiT ] BEiT: BERT Pre-Training of Image Transformers (ICLR 2022) [Paper] [CODE]
• [ DMAE ] Denoising Masked AutoEncoders Help Robust Classification (ICLR 2023) [Paper] [CODE]
• [ AttnMask ] What to Hide from Your Students: Attention-Guided Masked Image Modeling (ECCV 2022) [Paper] [CODE]
• [ SparK ] Designing BERT for Convolutional Networks: Sparse and Hierarchical Masked Modeling (ICLR 2023) [Paper] [CODE]
• [ CIM ] Corrupted Image Modeling for Self-Supervised Visual Pre-Training (ICLR 2023) [Paper]
• [ MixAE ] Mixed Autoencoder for Self-supervised Visual Representation Learning (CVPR 2023) [Paper]
• [ MixMIM ] MixMAE: Mixed and Masked Autoencoder for Efficient Pretraining of Hierarchical Vision Transformers (CVPR 2023) [Paper] [CODE]
• [ DropMAE ] DropMAE: Masked Autoencoders with Spatial-Attention Dropout for Tracking Tasks (CVPR 2023) [Paper] [CODE]
• [ iTPN ] Integrally Pre-Trained Transformer Pyramid Networks. (CVPR 2023) [Paper] [CODE]
• [ ConMIM ] Masked Image Modeling with Denoising Contrast. (ICLR 2023) [Paper] [CODE]
• [ MultiMAE ] MultiMAE: Multi-modal Multi-task Masked Autoencoders. (ICLR 2023) [Paper] [CODE]
• [ LCO ] Learning to cluster in order to transfer across domains and tasks (ICLR 2018) [Paper] [CODE]
• [ TinyMIM ] TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models. (CVPR2023) [Paper] [CODE]

• [ JisawNet ] Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles. (ECCV 2016) [Paper] [CODE]
• [ Colorful ] Colorful Image Colorization. (ECCV 2016) [Paper] [CODE]
• [ Colorfulv2] Colorization as a Proxy Task for Visual Understanding. (CVPR 2017) [Paper] [CODE]
• [ DeepPermNet] DeepPermNet: Visual Permutation Learning. (CVPR 2017) [Paper] [CODE]
• [ NAT ] Unsupervised Learning by Predicting Noise. (ICML 2017) [Paper] [CODE]
• [ OPN ] Unsupervised Representation Learning by Sorting Sequences. (ICCV 2017) [Paper] [CODE]
• [ Damage JisawNet ] Learning Image Representations by Completing Damaged Jigsaw Puzzles. (WACV 2018) [Paper] [CODE]
• [ Rotation ] Unsupervised Representation Learning by Predicting Image Rotations. (ICLR 2018) [Paper] [CODE]

• [ SINC ] SINC: Self-Supervised In-Context Learning for Vision-Language Tasks (ICCV 2023) [Paper]

• [ CDS ] CDS: Cross-Domain Self-supervised Pre-training (ICCV 2021) [Paper] [CODE]
• [ Deja Vu ] Deja Vu: Continual Model Generalization for Unseen Domains (ICLR 2023) [Paper] [CODE]
• [ FlexPredict ] Predicting masked tokens in stochastic locations improves masked image modeling (ArXiv 2023) [Paper]

• [ CutPaste ] CutPaste: Self-Supervised Learning for Anomaly Detection and Localization (CVPR 2021) [Paper] [CODE]
• [ SPot ] SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation (ECCV 2022) [Paper] [CODE]

• [ MuST ] Multi-Task Self-Training for Learning General Representations (ICCV 2021) [Paper]
• [ SMART ] SMART: Self-supervised Multi-task pretrAining with contRol Transformers (ICLR 2023) [Paper] [CODE]

• [ Few-shot ] When Does Self-supervision Improve Few-shot Learning? (ECCV 2020) [Paper] [CODE]
• [ Pareto ] Pareto Self-Supervised Training for Few-Shot Learning (CVPR 2021) [Paper]

• [ JULE ] Joint Unsupervised Learning of Deep Representations and Image Clusters. (CVPR 2016) [Paper] [CODE]
• [ Deep Cluster ] Deep Clustering for Unsupervised Learning of Visual Features (ECCV 2018) [Paper] [CODE]
• [ Self Cluster ] Self-labelling via simultaneous clustering and representation learning (ICLR 2020) [Paper] [CODE]
• [ ClusterFit ] Improving Generalization of Visual Representations (CVPR 2020) [Paper]
• [ SCAN ] SCAN: Learning to Classify Images without Labels (ECCV 2020) [Paper] [CODE]
• [ MisMatch ] Mitigating embedding and class assignment mismatch in unsupervised image classification (ECCV 2020) [Paper] [CODE]
• [ RUC ] Improving Unsupervised Image Clustering With Robust Learning (CVPR 2021) [Paper] [CODE]
• [ MICE ] MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering (ICLR 2021) [Paper] [CODE]
• [ GATCluster ] GATCluster: Self-Supervised Gaussian-Attention Network for Image Clustering (ECCV 2020) [Paper]
• [ Jigsaw Cluster ] Jigsaw Clustering for Unsupervised Visual Representation Learning (CVPR 2021) [Paper] [CODE]

• Self-supervised learning: The dark matter of intelligence (FAIR2022)

• ImageNet1K
• CUB-200
• Food-101
• Stanford-Car
• FGVC-Aircraft