| ID | Paper |
|---|
| 1 | The Role of Local Alignment and Uniformity in Image-Text Contrastive Learning on Medical Images
Philip Müller, Georgios Kaissis, Daniel Rueckert |
| 3 | Loss Landscape of Self-Supervised Learning
Liu Ziyin, Ekdeep Singh Lubana, Masahito Ueda, Hidenori Tanaka |
| 4 | Self-Supervised Pre-training with Transformers for Object Detection
Guoqiang Jin, Fan Yang, Mingshan Sun, Yakun Liu, Wei Li, Tianpeng Bao, Rui Zhao, Liwei Wu |
| 6 | Local Pseudo-Attributes for Long-Tailed Recognition
Dong-Jin Kim, Tsung-Wei Ke, Stella X. Yu |
| 7 | Simplicial Embeddings in Self-Supervised Learning and Downstream Classification
Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Ankit Vani, Michael Noukhovitch, Kenji Kawaguchi, Aaron Courville |
| 8 | Contrastive Learning for Multi-Label Classification
Vladimir Zaigrajew, Maciej Zieba |
| 9 | Matryoshka Representation Learning
Aniket Rege, Aditya Kusupati, Gantavya Bhatt, Matthew Wallingford, Aditya Sinha, Vivek Ramanujan, William Howard-Snyder, Kaifeng Chen, Sham M. Kakade, Prateek Jain, Ali Farhadi |
| 10 | Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods
Skanda Koppula, Yazhe Li, Evan Shelhamer, Andrew Jaegle, Nikhil Parthasarathy, Relja Arandjelovic, Joao Carreira, Olivier J Henaff |
| 11 | Improving Dense Contrastive Learning with Dense Negative Pairs
Berk Iskender, Zhenlin Xu, Simon Kornblith, En-Hung Chu, Maryam Khademi |
| 12 | Region Proposal Network Pre-Training Helps Label-Efficient Object Detection
Linus Ericsson, Nanqing Dong, Yongxin Yang, Ales Leonardis, Steven McDonagh |
| 13 | Super-Resolution through StyleGAN Regularized Latent Search
Marzieh Gheisari, Auguste Genovesio |
| 14 | Rethinking Benchmarking Framework of Self-Supervised Learning Approaches for Anomaly Localization
Tryambak Gangopadhyay, Sungmin Hong, Sujoy Roy, Yash Shah, Lin Lee Cheong |
| 15 | OmniMAE: Single Model Masked Pretraining on Images and Videos
Rohit Girdhar, Alaaeldin El-Nouby, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra |
| 17 | Transformed Autoencoder: Pre-training with Mask-Free Encoder and Transformed Decoder
Yichen Zhou, Pan Zhou, Chenyang Si, Weihao Yu, Teck Khim Ng, Shuicheng YAN |
| 18 | Mugs: A Multi-Granular Self-Supervised Learning Framework
Pan Zhou, Yichen Zhou, Chenyang Si, Weihao Yu, Teck Khim Ng, Shuicheng YAN |
| 19 | Time Series Anomaly Detection using Skip-Step Contrastive Predictive Coding
Kexin Zhang, Qingsong Wen, Chaoli Zhang, Liang Sun, Yong Liu |
| 20 | Why Do Self-Supervised Models Transfer? On the Impact of Invariance on Downstream Tasks
Linus Ericsson, Henry Gouk, Timothy Hospedales |
| 22 | Contrastive Self-supervised Learning via Minimizing Distance between Cosine Similarities of Negative Pair
Seongho Jeong, Heechul Jung |
| 24 | Towards Understanding Why Mask-Reconstruction Pretraining Helps in Downstream Tasks
Jiachun Pan, Pan Zhou, Shuicheng YAN |
| 25 | Self-Guided Diffusion Models
Vincent Tao Hu, David W Zhang, Yuki M Asano, Gertjan J. Burghouts, Cees G. M. Snoek |
| 26 | DUEL: Adaptive Duplicate Elimination on Working Memory for Self-Supervised Learning
Won-Seok Choi, Dong-Sig Han, Hyundo Lee, Junseok Park, Byoung-Tak Zhang |
| 27 | Content suppresses style: dimensionality collapse in contrastive learning
Evgenia Rusak, Patrik Reizinger, Roland S. Zimmermann, Wieland Brendel |
| 28 | Coreset Sampling from Open-Set for Fine-Grained Self-Supervised Learning
Sungnyun Kim, Sangmin Bae, Se-Young Yun |
| 30 | Unpaired Image-to-Image Translation with Limited Data to Reveal Subtle Phenotypes
Anis Bourou, Auguste Genovesio |
| 31 | Joint Embedding Predictive Architectures Focus on Slow Features
Vlad Sobal, Jyothir S V, Siddhartha Jalagam, Nicolas Carion, Kyunghyun Cho, Yann LeCun |
| 32 | Positive unlabeled learning with tensor networks
Bojan Žunkovič |
| 33 | VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
Yecheng Jason Ma, Shagun Sodhani, Dinesh Jayaraman, Osbert Bastani, Vikash Kumar, Amy Zhang |
| 34 | Multimodal contrastive learning for remote sensing tasks
Umangi Jain, Alex Wilson, Varun Gulshan |
| 35 | Clinical Contrastive Learning for Biomarker Detection
Kiran Premdat Kokilepersaud, Mohit Prabhushankar, Ghassan AlRegib |
| 36 | Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer
Andrius Ovsianas, Jason Ramapuram, Dan Busbridge, Eeshan Gunesh Dhekane, Russ Webb |
| 37 | SL3D: Self-supervised-Self-labeled 3D Recognition
Fernando Julio Cendra, Lan Ma, Jiajun Shen, XIAOJUAN QI |
| 38 | Contrastive Self-supervision Defines General-Purpose Similarity Functions
Charles Guille-Escuret, Pau Rodriguez, David Vazquez, Ioannis Mitliagkas, Joao Monteiro |
| 39 | Pitfalls of Gaussians as a noise distribution in NCE
Holden Lee, Chirag Pabbaraju, Anish Prasad Sevekari, Andrej Risteski |
| 40 | Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization
Omead Pooladzandi, Pasha Khosravi, Erik Nijkamp, Baharan Mirzasoleiman |
| 41 | A Theoretical Study of Inductive Biases in Contrastive Learning
Jeff Z. HaoChen, Tengyu Ma |
| 42 | Towards Sustainable Self-supervised Learning
Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng YAN |
| 43 | Towards Unsupervised Visual Reasoning: Do Off-The-Shelf Features Know How to Reason?
Monika Wysoczańska, Tom Monnier, Tomasz Trzcinski, David Picard |
| 44 | An eigenspace view reveals how predictor networks and stop-grads provide implicit variance regularization
Manu Srinath Halvagal, Axel Laborieux, Friedemann Zenke |
| 45 | Towards Self-Supervised Learning for Prediction of Vital Status of Colorectal Cancer Patients
Sathwik Acharya, Om Amitesh Boggaram Ravishankar, Murali Krishna Madan Rao, Girivinay Padegal, Gowri Srinivasa |
| 46 | On the Role of Nonlinearity in Training Dynamics of Contrastive Learning on One-layer Network
Yuandong Tian |
| 47 | Homomorphic Self-Supervised Learning
T. Anderson Keller, Xavier Suau, Luca Zappella |
| 48 | Leveraging the Third Dimension in Contrastive Learning
Sumukh K Aithal, Anirudh Goyal, Alex Lamb, Yoshua Bengio, Michael Curtis Mozer |
| 49 | Guiding Energy-based Models via Contrastive Latent Variables
Hankook Lee, Jongheon Jeong, Sejun Park, Jinwoo Shin |
| 50 | Understanding contrastive versus reconstructive self-supervised learning of Vision Transformers
Shashank Shekhar, Florian Bordes, Pascal Vincent, Ari S. Morcos |
| 51 | Contrastive Self-Supervised Learning for Skeleton Representations
Nico Lingg, Miguel Sarabia, Luca Zappella, Barry-John Theobald |
| 52 | Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning
Martin Q. Ma, Yao-Hung Hubert Tsai, Paul Pu Liang, Han Zhao, Kun Zhang, Ruslan Salakhutdinov, Louis-Philippe Morency |
| 54 | Improving self-supervised representation learning via sequential adversarial masking
Dylan Sam, Min Bai, Tristan James McKinney, Li Erran Li |
| 56 | When does visual self-supervision aid adversarial training in improving adversarial robustness?
Michal Kucer, Diane Oyen, Garrett T. Kenyon |
| 57 | Lovasz Theta Contrastive Learning
Georgios Smyrnis, Matt Jordan, Ananya Uppal, Giannis Daras, Alex Dimakis |
| 58 | Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction
Bhawesh Kumar, Anil Palepu, Rudraksh Tuwani, Andrew Beam |
| 59 | Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models
Hong Liu, Sang Michael Xie, Zhiyuan Li, Tengyu Ma |
| 60 | Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning
Alex Tamkin, Margalit Glasgow, Xiluo He, Noah Goodman |
| 61 | Understanding and Improving the Role of Projection Head in Self-Supervised Learning
Kartik Gupta, Thalaiyasingam Ajanthan, Anton van den Hengel, Stephen Gould |
| 64 | Variational Energy-Based Models: A Probabilistic Framework for Contrastive Self-Supervised Learning
Tianqi Du, Yifei Wang, Weiran Huang, Yisen Wang |
| 65 | Does Structural Attention Improve Compositional Representations in Vision-Language Models?
Rohan Pandey, Rulin Shao, Paul Pu Liang, Louis-Philippe Morency, Ruslan Salakhutdinov |
| 66 | Visual Pre-training for Navigation: What Can We Learn from Noise?
Yanwei Wang, Ching-Yun Ko, Pulkit Agrawal |
| 67 | UniCon: Unidirectional Split Learning with Contrastive Loss for Visual Question Answering
Yuwei Sun, Hideya Ochiai |
| 68 | AggNCE: Asymptotically Identifiable Contrastive Learning
Jingyi Cui, Weiran Huang, Yifei Wang, Yisen Wang |
| 69 | CASS: Cross Architectural Self-Supervision for Medical Image Analysis
Pranav Singh, Elena Sizikova, Jacopo Cirrone |
| 70 | Can we train vision and language zero-shot classification models without syntax?
Ajinkya Tejankar, Maziar Sanjabi, Bichen Wu, Madian Khabsa, Saining Xie, Hamed Pirsiavash, Hamed Firooz |
| 72 | Exploring Spurious Learning in Self-Supervised Representations
Kimia Hamidieh, Haoran Zhang, Marzyeh Ghassemi |
| 74 | TS-Rep: Self-supervised time series representation learning from robot sensor data
Pratik Somaiya, Harit Pandya, Riccardo Polvara, Marc Hanheide, Grzegorz Cielniak |