zju-vipa / Fast-DatafreeLinks
[AAAI-2022] Up to 100x Faster Data-free Knowledge Distillation
☆74Updated 3 years ago
Alternatives and similar repositories for Fast-Datafree
Users that are interested in Fast-Datafree are comparing it to the libraries listed below
Sorting:
- [IJCAI-2021] Contrastive Model Inversion for Data-Free Knowledge Distillation☆72Updated 3 years ago
- ☆87Updated 2 years ago
- Official PyTorch implementation of "Dataset Condensation via Efficient Synthetic-Data Parameterization" (ICML'22)☆113Updated 2 years ago
- Efficient Dataset Distillation by Representative Matching☆113Updated last year
- Data-Free Network Quantization With Adversarial Knowledge Distillation PyTorch☆30Updated 4 years ago
- [TPAMI 2023] Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny Subspaces☆42Updated 3 years ago
- An Numpy and PyTorch Implementation of CKA-similarity with CUDA support☆94Updated 4 years ago
- Data-Free Knowledge Distillation☆22Updated 3 years ago
- [ICLR 2023] Trainable Weight Averaging: Efficient Training by Optimizing Historical Solutions☆27Updated 8 months ago
- This is a method of dataset condensation, and it has been accepted by CVPR-2022.☆71Updated last year
- Official implementation of "When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture" published at Neur…☆34Updated last year
- Knowledge distillation (KD) from a decision-based black-box (DB3) teacher without training data.☆22Updated 3 years ago
- Reimplmentation of Visualizing the Loss Landscape of Neural Nets with PyTorch 1.8☆29Updated 2 years ago
- This repository is the official implementation of Dataset Condensation with Contrastive Signals (DCC), accepted at ICML 2022.☆22Updated 3 years ago
- PyTorch implementation of paper "Dataset Distillation via Factorization" in NeurIPS 2022.☆66Updated 2 years ago
- ☆14Updated 2 years ago
- Code and pretrained models for paper: Data-Free Adversarial Distillation☆99Updated 2 years ago
- Code and checkpoints of compressed networks for the paper titled "HYDRA: Pruning Adversarially Robust Neural Networks" (NeurIPS 2020) (ht…☆91Updated 2 years ago
- Query-Efficient Data-Free Learning from Black-Box Models☆22Updated 2 years ago
- This is the source code for Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score (ICML2023).☆38Updated last year
- ICLR 2024, Towards Lossless Dataset Distillation via Difficulty-Aligned Trajectory Matching☆102Updated last year
- ☆23Updated last year
- (Pytorch) Training ResNets on ImageNet-100 data☆64Updated 3 years ago
- [ICLR2023] Towards Understanding and Mitigating Dimensional Collapse in Heterogeneous Federated Learning (https://arxiv.org/abs/2210.0022…☆40Updated 2 years ago
- [CVPR 2022 oral] Subspace Adversarial Training☆27Updated 2 years ago
- This is the code of ICLR 2022 Oral paper 'Non-Transferable Learning: A New Approach for Model Ownership Verification and Applicability Au…☆30Updated 2 years ago
- [NeurIPS 2021] “When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?”☆48Updated 3 years ago
- The code of the paper "Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation" (CVPR2023)☆40Updated 2 years ago
- Official Code for Dataset Distillation using Neural Feature Regression (NeurIPS 2022)☆48Updated 2 years ago
- [NeurIPS-2021] Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data☆45Updated 3 years ago