SRe2L
(NeurIPS 2023 spotlight) Large-scale Dataset Distillation/Condensation, 50 IPC (Images Per Class) achieves the highest 60.8% on original ImageNet-1K val set.
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- method.png
- results.png
- vis_compare.png
- README.md
- recover_cda_in1k.py
- recover_cda_in21k.py
- recover_cda_tiny.py
- utils.py
- imagenet_ipc.py
- post_cifar100.py
- utils.py
- recover_cifar100.py
- utils.py
- dataset_v2.py
- train_kd.py
- utils.py
- data_synthesis.py
- utils.py
- concat_show.gif
- concat_show_cifar.gif
- install.md
- method.jpg
- parameter.png
- performance.png
- vis_all.jpg
- README.md
- imagenet_ipc.py
- README_CIFAR.md
- README_TINY.md
- recover_cifar.py
- recover_cifar.sh
- recover_tiny.py
- recover_tiny.sh
- relabel_cifar.py
- relabel_cifar.sh
- squeeze_cifar.py
- squeeze_cifar.sh
- utils.py
- animation.gif
- fkd-mix.png
- overview.png
- recover.png
- relabel.png
- results.png
- squeeze.png
- .gitignore
- data_synthesis.py
- data_utils.py
- multi_gput_recover.sh
- README.md
- utils.py
- data_synthesis.py
- README.md
- recover.sh
- utils.py
- __init__.py
- generate_soft_label.py
- README.md
- relabel.sh
- utils_fkd.py
- imagenet_ipc.py
- README.md
- train_FKD.py
- train_FKD.sh
- train_KD.py
- train_KD.sh
- utils.py
- README.md
- .gitignore
- README.md
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