Self Supervised Learning Overview
📜 Self-Supervised Learning from Images: Up-to-date reading list.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Transferlearning | 12,494 | 3 months ago | 14 | mit | Python | |||||
| Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习 | ||||||||||
| Simclr | 3,525 | a year ago | 1 | April 14, 2022 | 69 | apache-2.0 | Jupyter Notebook | |||
| SimCLRv2 - Big Self-Supervised Models are Strong Semi-Supervised Learners | ||||||||||
| Simclr | 1,745 | a year ago | 22 | mit | Jupyter Notebook | |||||
| PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations | ||||||||||
| Unsupervised Classification | 1,217 | 9 months ago | 18 | other | Python | |||||
| SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020] | ||||||||||
| Awesome Graph Self Supervised Learning | 1,194 | 4 months ago | 1 | |||||||
| Awesome Graph Self-Supervised Learning | ||||||||||
| Awesome Contrastive Self Supervised Learning | 1,029 | 6 months ago | 1 | |||||||
| A comprehensive list of awesome contrastive self-supervised learning papers. | ||||||||||
| Simclr | 675 | 5 months ago | 3 | January 23, 2021 | 11 | mit | Python | |||
| PyTorch implementation of SimCLR: A Simple Framework for Contrastive Learning of Visual Representations by T. Chen et al. | ||||||||||
| Disentangling Vae | 668 | a year ago | 7 | other | Python | |||||
| Experiments for understanding disentanglement in VAE latent representations | ||||||||||
| Lemniscate.pytorch | 467 | 4 years ago | 9 | Python | ||||||
| Unsupervised Feature Learning via Non-parametric Instance Discrimination | ||||||||||
| Awesome Vaes | 448 | 3 years ago | ||||||||
| A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models. | ||||||||||
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