vasusingla / simple-data-attribution
A simple and efficient baseline for data attribution
☆11Updated last year
Alternatives and similar repositories for simple-data-attribution:
Users that are interested in simple-data-attribution are comparing it to the libraries listed below
- A modern look at the relationship between sharpness and generalization [ICML 2023]☆43Updated last year
- Pytorch ImageNet1k Loader with Bounding Boxes.☆12Updated 3 years ago
- What do we learn from inverting CLIP models?☆49Updated 11 months ago
- [CVPR 2024] This repository includes the official implementation our paper "Revisiting Adversarial Training at Scale"☆18Updated 9 months ago
- Official Implementation for PlugIn Inversion☆16Updated 3 years ago
- Code for the paper "A Light Recipe to Train Robust Vision Transformers" [SaTML 2023]☆53Updated 2 years ago
- Official repo for the paper "Make Some Noise: Reliable and Efficient Single-Step Adversarial Training" (https://arxiv.org/abs/2202.01181)☆25Updated 2 years ago
- Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"☆21Updated last year
- Training vision models with full-batch gradient descent and regularization☆37Updated 2 years ago
- ☆34Updated last year
- ☆17Updated 2 years ago
- Robust Principles: Architectural Design Principles for Adversarially Robust CNNs☆21Updated last year
- Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet☆30Updated last year
- Certified Patch Robustness via Smoothed Vision Transformers☆42Updated 3 years ago
- Code relative to "Adversarial robustness against multiple and single $l_p$-threat models via quick fine-tuning of robust classifiers"☆18Updated 2 years ago
- [NeurIPS'22] Official Repository for Characterizing Datapoints via Second-Split Forgetting☆14Updated last year
- ☆33Updated last year
- ☆14Updated 11 months ago
- ☆30Updated 2 months ago
- Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion☆11Updated 10 months ago
- On the effectiveness of adversarial training against common corruptions [UAI 2022]☆30Updated 2 years ago
- Official Pytorch repo of CVPR'23 and NeurIPS'23 papers on understanding replication in diffusion models.☆105Updated last year
- Intriguing Properties of Data Attribution on Diffusion Models (ICLR 2024)☆28Updated last year
- Sharpness-Aware Minimization Leads to Low-Rank Features [NeurIPS 2023]☆27Updated last year
- Code for the paper "Evading Black-box Classifiers Without Breaking Eggs" [SaTML 2024]☆20Updated 10 months ago
- ☆57Updated 2 years ago
- Code for T-MARS data filtering☆35Updated last year
- Helper-based Adversarial Training: Reducing Excessive Margin to Achieve a Better Accuracy vs. Robustness Trade-off☆29Updated 2 years ago
- [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”☆48Updated 3 years ago
- Pytorch implementation of Adversarially Robust Distillation (ARD)☆59Updated 5 years ago