vimar-gu / MinimaxDiffusion
[CVPR2024] Efficient Dataset Distillation via Minimax Diffusion
☆91Updated 11 months ago
Alternatives and similar repositories for MinimaxDiffusion:
Users that are interested in MinimaxDiffusion are comparing it to the libraries listed below
- (NeurIPS 2023 spotlight) Large-scale Dataset Distillation/Condensation, 50 IPC (Images Per Class) achieves the highest 60.8% on original …☆125Updated 4 months ago
- [CVPR 2024] On the Diversity and Realism of Distilled Dataset: An Efficient Dataset Distillation Paradigm☆62Updated 3 weeks ago
- ICLR 2024, Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching☆102Updated 9 months ago
- [CVPR2024 highlight] Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching (G-VBSM)☆27Updated 5 months ago
- Efficient Dataset Distillation by Representative Matching☆112Updated last year
- A pytorch implementation of CVPR24 paper "D4M: Dataset Distillation via Disentangled Diffusion Model"☆28Updated 6 months ago
- Elucidated Dataset Condensation (NeurIPS 2024)☆19Updated 5 months ago
- Official implementation for paper "Knowledge Diffusion for Distillation", NeurIPS 2023☆81Updated last year
- Data distillation benchmark☆57Updated this week
- Prioritize Alignment in Dataset Distillation☆20Updated 3 months ago
- ☆113Updated last year
- ☆53Updated 2 months ago
- ☆42Updated last year
- Code for our ICML'24 on multimodal dataset distillation☆36Updated 5 months ago
- source code for NeurIPS'23 paper "Dream the Impossible: Outlier Imagination with Diffusion Models"☆66Updated last month
- The official implementation of "2024NeurIPS Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation"☆42Updated 2 months ago
- The code of the paper "Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation" (CVPR2023)☆40Updated last year
- Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment, arXiv 2024 / CVPR 2025☆23Updated 2 weeks ago
- Official PyTorch Code for "Is Synthetic Data From Diffusion Models Ready for Knowledge Distillation?" (https://arxiv.org/abs/2305.12954)☆46Updated last year
- Code for "Training on Thin Air: Improve Image Classification with Generated Data"☆45Updated last year
- [ICCV 2023] DataDAM: Efficient Dataset Distillation with Attention Matching☆33Updated 8 months ago
- ☆15Updated 9 months ago
- PyTorch implementation of paper "Sparse Parameterization for Epitomic Dataset Distillation" in NeurIPS 2023.☆20Updated 8 months ago
- [ICCV 2023 & AAAI 2023] Binary Adapters & FacT, [Tech report] Convpass☆179Updated last year
- ☆106Updated last year
- [ICLR 2024] Real-Fake: Effective Training Data Synthesis Through Distribution Matching☆79Updated last year
- Diffusion-TTA improves pre-trained discriminative models such as image classifiers or segmentors using pre-trained generative models.☆68Updated 11 months ago
- [NeurIPS'22] This is an official implementation for "Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning".☆177Updated last year
- Official implementation of AAAI 2023 paper "Parameter-efficient Model Adaptation for Vision Transformers"☆104Updated last year
- Official repository of "Back to Source: Diffusion-Driven Test-Time Adaptation"☆77Updated last year