Updates to the Model Documentations and addition of MACL Loss
Changes
- Docs: Fix mismatch between forward method and docstring of
NTXentLossby @adosar in #1789 - Update NNCLR model examples in docs
- Updated the BYOL model examples in docs
- Updated the DINO model examples in docs
- Update the SimSiam model examples in docs
- Additional tests added to pooling operation for DetCon
- Fix issues with the Lightning Trainer's strategy in the MAE examples and support new Lightning versions in the benchmarks
- Added loss function for MACL (Model-Aware Contrastive Learning)
- Updated CONTRIBUTING Guide and GitHub Actions
- Fix multiple issues with loss tests
Models
- AIM: Scalable Pre-training of Large Autoregressive Image Models
- Barlow Twins: Self-Supervised Learning via Redundancy Reduction, 2021
- Bootstrap your own latent: A new approach to self-supervised Learning, 2020
- DCL: Decoupled Contrastive Learning, 2021
- DenseCL: Dense Contrastive Learning for Self-Supervised Visual Pre-Training, 2021
- DINO: Emerging Properties in Self-Supervised Vision Transformers, 2021
- FastSiam: Resource-Efficient Self-supervised Learning on a Single GPU, 2022
- I-JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture, 2023
- MAE: Masked Autoencoders Are Scalable Vision Learners, 2021
- MSN: Masked Siamese Networks for Label-Efficient Learning, 2022
- MoCo: Momentum Contrast for Unsupervised Visual Representation Learning, 2019
- NNCLR: Nearest-Neighbor Contrastive Learning of Visual Representations, 2021
- PMSN: Prior Matching for Siamese Networks, 2022
- SimCLR: A Simple Framework for Contrastive Learning of Visual Representations, 2020
- SimMIM: A Simple Framework for Masked Image Modeling, 2021
- SimSiam: Exploring Simple Siamese Representation Learning, 2020
- SMoG: Unsupervised Visual Representation Learning by Synchronous Momentum Grouping, 2022
- SwAV: Unsupervised Learning of Visual Features by Contrasting Cluster Assignments, M. Caron, 2020
- TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning, 2022
- VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning, Bardes, A. et. al, 2022
- VICRegL: VICRegL: Self-Supervised Learning of Local Visual Features, 2022
Contributors
adosar