Alternatives and similar repositories for RWP

Users that are interested in RWP are comparing it to the libraries listed below

• mueller-mp / SAM-ON
  ☆34Updated last year
• AngusDujw / SAF
  ☆36Updated 2 years ago
• dydjw9 / Efficient_SAM
  ☆58Updated 2 years ago
• Saehyung-Lee / DCC
  This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC), accepted at ICML 2022.
  ☆22Updated 3 years ago
• x-zho14 / ProbMask-official
  Implementation of Effective Sparsification of Neural Networks with Global Sparsity Constraint
  ☆31Updated 3 years ago
• tml-epfl / sam-low-rank-features
  Sharpness-Aware Minimization Leads to Low-Rank Features [NeurIPS 2023]
  ☆28Updated last year
• rgeirhos / dataset-pruning-metrics
  Metrics for "Beyond neural scaling laws: beating power law scaling via data pruning " (NeurIPS 2022 Outstanding Paper Award)
  ☆56Updated 2 years ago
• UCSC-VLAA / AdvXL
  [CVPR 2024] This repository includes the official implementation our paper "Revisiting Adversarial Training at Scale"
  ☆20Updated last year
• nblt / DLDR
  [TPAMI 2023] Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny Subspaces
  ☆42Updated 3 years ago
• tml-epfl / understanding-sam
  Towards Understanding Sharpness-Aware Minimization [ICML 2022]
  ☆35Updated 3 years ago
• JeanKaddour / WASAM
  Weight-Averaged Sharpness-Aware Minimization (NeurIPS 2022)
  ☆28Updated 2 years ago
• deeplearning-wisc / Spurious_OOD
  ☆23Updated 3 years ago
• amazon-science / normalizer-free-robust-training
  Official implementation of "Removing Batch Normalization Boosts Adversarial Training" (ICML'22)
  ☆19Updated 3 years ago
• snu-mllab / Efficient-Dataset-Condensation
  Official PyTorch implementation of "Dataset Condensation via Efficient Synthetic-Data Parameterization" (ICML'22)
  ☆113Updated last year
• devansh20la / LPF-SGD
  ☆17Updated 2 years ago
• yolky / RFAD
  Code for the paper "Efficient Dataset Distillation using Random Feature Approximation"
  ☆37Updated 2 years ago
• ehuynh1106 / TinyImageNet-Transformers
  Transformers trained on Tiny ImageNet
  ☆56Updated 2 weeks ago
• dedeswim / vits-robustness-torch
  Code for the paper "A Light Recipe to Train Robust Vision Transformers" [SaTML 2023]
  ☆52Updated 2 years ago
• justincui03 / dc_benchmark
  ☆86Updated 2 years ago
• nblt / TWA
  [ICLR 2023] Trainable Weight Averaging: Efficient Training by Optimizing Historical Solutions
  ☆27Updated 6 months ago
• tml-epfl / sharpness-vs-generalization
  A modern look at the relationship between sharpness and generalization [ICML 2023]
  ☆43Updated last year
• MarlonBecker / MSAM
  ☆19Updated last year
• google-research / understanding-curricula
  ☆34Updated 2 months ago
• mil-ad / prospr
  Prospect Pruning: Finding Trainable Weights at Initialization Using Meta-Gradients
  ☆32Updated 3 years ago
• deeplearning-wisc / gradnorm_ood
  On the Importance of Gradients for Detecting Distributional Shifts in the Wild
  ☆56Updated 3 years ago
• Mi-Peng / Sparse-Sharpness-Aware-Minimization
  [NeurIPS 2022] Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach -- Official Implementation
  ☆45Updated 2 years ago
• boone891214 / MEST
  [NeurIPS‘2021] "MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge", Geng Yuan, Xiaolong Ma, Yanzhi Wang et al…
  ☆18Updated 3 years ago
• VITA-Group / Alleviate-Robust-Overfitting
  [ICLR 2021] "Robust Overfitting may be mitigated by properly learned smoothening" by Tianlong Chen*, Zhenyu Zhang*, Sijia Liu, Shiyu Chan…
  ☆47Updated 3 years ago
• tanganke / opcm
  ☆16Updated 7 months ago
• JonasGeiping / fullbatchtraining
  Training vision models with full-batch gradient descent and regularization
  ☆37Updated 2 years ago