AlexeyGB / batch-norm-helps-optimization
Reproduction of "How Does Batch Normalization Help Optimization?" paper
☆21Updated 6 years ago
Alternatives and similar repositories for batch-norm-helps-optimization
Users that are interested in batch-norm-helps-optimization are comparing it to the libraries listed below
Sorting:
- Learning recognition/segmentation models without end-to-end training. 40%-60% less GPU memory footprint. Same training time. Better perfo…☆90Updated 2 years ago
- [CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jon…☆69Updated 2 years ago
- Code for ViTAS_Vision Transformer Architecture Search☆50Updated 3 years ago
- PyTorch implementation of our Adam-NSCL algorithm from our CVPR2021 (oral) paper "Training Networks in Null Space for Continual Learning"☆61Updated 3 years ago
- An efficient implementation for ImageNet classification☆17Updated 4 years ago
- [NeurIPS 2020] "Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free" by Haotao Wang*, Tianlong C…☆44Updated 3 years ago
- ☆30Updated 4 years ago
- released code for the paper: ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding☆31Updated 4 years ago
- PyTorch implementation of Weighted Batch-Normalization layers☆37Updated 4 years ago
- Bag of Instances Aggregation Boosts Self-supervised Distillation (ICLR 2022)☆33Updated 3 years ago
- This repository provides code for "On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness".☆45Updated 2 years ago
- This code accompanies the paper "Parameter-free Online Test-time Adaptation".☆70Updated 2 years ago
- ☆24Updated 3 years ago
- This is a method of dataset condensation, and it has been accepted by CVPR-2022.☆69Updated last year
- ReSSL: Relational Self-Supervised Learning with Weak Augmentation☆57Updated 3 years ago
- ☆32Updated 3 years ago
- Code for our ICLR'2022 paper "Generalizing Few-Shot NAS with Gradient Matching"☆22Updated 2 years ago
- Code for our paper "Informative Dropout for Robust Representation Learning: A Shape-bias Perspective" (ICML 2020)☆125Updated 2 years ago
- Weight-Averaged Sharpness-Aware Minimization (NeurIPS 2022)☆28Updated 2 years ago
- The Official Implementation of the ICCV-2021 Paper: Semantically Coherent Out-of-Distribution Detection.☆70Updated 3 years ago
- Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation Driven Search', accepted by ICLR 2021.☆56Updated 3 years ago
- ☆45Updated 3 years ago
- ☆84Updated 3 years ago
- (CVPR 2022) Automated Progressive Learning for Efficient Training of Vision Transformers☆25Updated 2 months ago
- ☆21Updated 5 years ago
- The project page of paper: Information Competing Process for Learning Diversified Representations [NeurIPS 2019]☆18Updated 5 years ago
- [ECCV2022] The PyTorch implementation of paper "Equivariance and Invariance Inductive Bias for Learning from Insufficient Data"☆19Updated 2 years ago
- CVPR2021: Out-of-Distribution Detection Using Union of 1-Dimensional Subspaces☆22Updated 3 years ago
- Code for our ICLR'2021 paper "DrNAS: Dirichlet Neural Architecture Search"☆44Updated 4 years ago
- Metrics for "Beyond neural scaling laws: beating power law scaling via data pruning " (NeurIPS 2022 Outstanding Paper Award)☆56Updated 2 years ago