ServiceNow / bilevel_augment
bilevel_augment is a ServiceNow Research project that was started at Element AI.
☆26Updated 2 years ago
Related projects ⓘ
Alternatives and complementary repositories for bilevel_augment
- ☆26Updated 3 years ago
- Official code for "Mean Shift for Self-Supervised Learning"☆55Updated 3 years ago
- [CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jon…☆68Updated last year
- Automatic model evaluation (AutoEval) in CVPR'21&TPAMI'22☆36Updated 2 years ago
- Official PyTorch implementation of "Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity" (ICLR'21 Oral)☆104Updated 2 years ago
- SKD : Self-supervised Knowledge Distillation for Few-shot Learning☆95Updated last year
- ☆31Updated 3 years ago
- Parametric Instance Classification for Unsupervised Visual Feature Learning, NeurIPS 2020☆51Updated 3 years ago
- Code for Active Mixup in 2020 CVPR☆22Updated 2 years ago
- Code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning", Ren et al., NeurIPS'20☆25Updated 3 years ago
- Trained model weights, training and evaluation code from the paper "A simple way to make neural networks robust against diverse image cor…☆62Updated last year
- Code for CVPR2021 paper: MOOD: Multi-level Out-of-distribution Detection☆38Updated last year
- PyTorch implementation of Weighted Batch-Normalization layers☆37Updated 4 years ago
- ☆42Updated 4 years ago
- ☆29Updated 3 years ago
- Code for the paper "Selecting Relevant Features from a Universal Representation for Few-shot Classification"☆41Updated 3 years ago
- [WACV21] Code for our paper: Samuel, Atzmon and Chechik, "From Generalized zero-shot learning to long-tail with class descriptors"☆28Updated 3 years ago
- Code for reproducing experiments in "How Useful is Self-Supervised Pretraining for Visual Tasks?"☆60Updated 3 months ago
- Out-of-distribution generalization benchmarks for image recognition models☆14Updated 4 years ago
- [ICML 2021] “ Self-Damaging Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Bobak Mortazavi, Zhangyang Wang☆63Updated 2 years ago
- Source code accompanying our CVPR 2019 paper: "NetTailor: Tuning the architecture, not just the weights."☆52Updated 3 years ago
- (L2ID@CVPR2021, TNNLS2022) Boosting Co-teaching with Compression Regularization for Label Noise☆46Updated last year
- ☆46Updated 3 years ago
- (NeurIPS 2020) Meta-Consolidation for Continual Learning☆36Updated 3 years ago
- ICLR 2021, Fair Mixup: Fairness via Interpolation☆55Updated 3 years ago
- Regularizing Meta-Learning via Gradient Dropout☆52Updated 4 years ago
- Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations. [NeurIPS 2021]☆88Updated 3 years ago
- (CVPR 2020) This repo contains code for "PADS: Policy-Adapted Sampling for Visual Similarity Learning", which proposes learnable triplet …☆60Updated 4 years ago
- Compressing Representations for Self-Supervised Learning☆78Updated 3 years ago