awesome-self-supervised-learning
github.com/lightly-ai/awesome-self-supervised-learning ↗Curated List of papers on Self-Supervised Representation Learning
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2022
- A Closer Look at Self-Supervised Lightweight Vision Transformers
- A simple, efficient and scalable contrastive masked autoencoder for learning visual representations
- Beyond neural scaling laws: beating power law scaling via data pruning
- Can CNNs Be More Robust Than Transformers?
- Improving Visual Representation Learning through Perceptual Understanding
- Is Self-Supervised Learning More Robust Than Supervised Learning?
2023
- A Cookbook of Self-Supervised Learning
- Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need
- An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization
- CycleCL: Self-supervised Learning for Periodic Videos
- DINOv2: Learning Robust Visual Features without Supervision
- DropPos: Pre-Training Vision Transformers by Reconstructing Dropped Positions
2024
- ARVideo: Autoregressive Pretraining for Self-Supervised Video Representation Learning
- Asymmetric Masked Distillation for Pre-Training Small Foundation Models
- Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
- GLID: Pre-training a Generalist Encoder-Decoder Vision Model
- Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations?
- Rethinking Patch Dependence for Masked Autoencoders
2020
2021
- Barlow Twins: Self-Supervised Learning via Redundancy Reduction
- Decoupled Contrastive Learning
- Dense Contrastive Learning for Self-Supervised Visual Pre-Training
- Efficient Visual Pretraining with Contrastive Detection
- Emerging Properties in Self-Supervised Vision Transformers
- Exploring Simple Siamese Representation Learning
Resources
Showing a sample of 66 resources. View the full list on GitHub →