MadryLab / journey-TRAK
Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"
☆21Updated last year
Alternatives and similar repositories for journey-TRAK:
Users that are interested in journey-TRAK are comparing it to the libraries listed below
- A modern look at the relationship between sharpness and generalization [ICML 2023]☆43Updated last year
- ☆28Updated 7 months ago
- Host CIFAR-10.2 Data Set☆13Updated 3 years ago
- ☆54Updated 4 years ago
- Towards Understanding Sharpness-Aware Minimization [ICML 2022]☆35Updated 2 years ago
- Source code of "What can linearized neural networks actually say about generalization?☆20Updated 3 years ago
- Distilling Model Failures as Directions in Latent Space☆46Updated 2 years ago
- ☆34Updated last year
- Intriguing Properties of Data Attribution on Diffusion Models (ICLR 2024)☆28Updated last year
- Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation☆44Updated last year
- Do input gradients highlight discriminative features? [NeurIPS 2021] (https://arxiv.org/abs/2102.12781)☆13Updated 2 years ago
- ☆17Updated 2 years ago
- ☆30Updated last month
- ☆107Updated last year
- Training vision models with full-batch gradient descent and regularization☆37Updated 2 years ago
- Provably (and non-vacuously) bounding test error of deep neural networks under distribution shift with unlabeled test data.☆9Updated 11 months ago
- Code for the ICLR 2022 paper. Salient Imagenet: How to discover spurious features in deep learning?☆38Updated 2 years ago
- ☆28Updated last year
- ☆14Updated 11 months ago
- ☆44Updated 2 years ago
- Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet☆30Updated last year
- This is an official repository for "LAVA: Data Valuation without Pre-Specified Learning Algorithms" (ICLR2023).☆46Updated 8 months ago
- The Pitfalls of Simplicity Bias in Neural Networks [NeurIPS 2020] (http://arxiv.org/abs/2006.07710v2)☆39Updated last year
- [ICLR'22] Self-supervised learning optimally robust representations for domain shift.☆23Updated 3 years ago
- An Investigation of Why Overparameterization Exacerbates Spurious Correlations☆30Updated 4 years ago
- What do we learn from inverting CLIP models?☆49Updated 11 months ago
- ☆39Updated 2 years ago
- Simple data balancing baselines for worst-group-accuracy benchmarks.☆41Updated last year
- A simple and efficient baseline for data attribution☆11Updated last year
- Code for the paper "Data Feedback Loops: Model-driven Amplification of Dataset Biases"☆15Updated 2 years ago