db-Lee / selfsup_ddLinks
Self-Supervised Dataset Distillation for Transfer Learning
☆16Updated last year
Alternatives and similar repositories for selfsup_dd
Users that are interested in selfsup_dd are comparing it to the libraries listed below
Sorting:
- Repository for research works and resources related to model reprogramming <https://arxiv.org/abs/2202.10629>☆61Updated last year
- The code of the paper "Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation" (CVPR2023)☆40Updated 2 years ago
- PyTorch implementation of CIDER (How to exploit hyperspherical embeddings for out-of-distribution detection), ICLR 2023☆61Updated last year
- [NeurIPS 2023] "Diversified Outlier Exposure for Out-of-Distribution Detection via Informative Extrapolation"☆11Updated last year
- This is the implementation of our CVPR'23 paper On the Pitfall of Mixup for Uncertainty Calibration. In the paper, we conduct a series of…☆18Updated 2 years ago
- This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC), accepted at ICML 2022.☆22Updated 3 years ago
- ☆86Updated 2 years ago
- Code for Model Agnostic Sample Reweighting for Out-of-Distribution Learning☆44Updated 2 years ago
- Code for "Surgical Fine-Tuning Improves Adaptation to Distribution Shifts" published at ICLR 2023☆29Updated 2 years ago
- Official PyTorch implementation of "Dataset Condensation via Efficient Synthetic-Data Parameterization" (ICML'22)☆113Updated last year
- Official PyTorch implementation of "Loss-Curvature Matching for Dataset Selection and Condensation" (AISTATS 2023)☆21Updated 2 years ago
- ☆40Updated 2 years ago
- [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”☆48Updated 3 years ago
- OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift. ICML 2024 and ICLRW-DMLR 2024☆22Updated last year
- Implementaiton of "DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation" (accepted by NAACL2024 Findings)".☆22Updated 5 months ago
- Code for paper: “What Data Benefits My Classifier?” Enhancing Model Performance and Interpretability through Influence-Based Data Selecti…☆23Updated last year
- Paper of out of distribution detection and generalization☆56Updated last year
- Official Pytorch implementation of "Learning Debiased Representation via Disentangled Feature Augmentation (Neurips 2021, Oral)"☆102Updated 2 years ago
- ☆27Updated 2 years ago
- [NeurIPS 2022] The official code for our NeurIPS 2022 paper "Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnab…☆45Updated 2 years ago
- ☆16Updated 3 years ago
- Official PyTorch implementation for Frequency Domain-based Dataset Distillation [NeurIPS 2023]☆30Updated last year
- Prioritize Alignment in Dataset Distillation☆20Updated 8 months ago
- ☆67Updated 2 years ago
- Official PyTorch implementation of "Multisize Dataset Condensation" (ICLR'24 Oral)☆14Updated last year
- ☆26Updated 2 years ago
- [NeurIPS 2023] "Learning to Augment Distributions for Out-of-distribution Detection"☆12Updated last year
- Official Implementation of Avoiding spurious correlations via logit correction☆17Updated 2 years ago
- [ICLR 2023] Is the Performance of My Deep Network Too Good to Be True? A Direct Approach to Estimating the Bayes Error in Binary Classifi…☆21Updated 3 months ago
- ☆16Updated last year