Papers with Code 아카이브
Dataset Condensation
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Condense the full dataset into a tiny set of synthetic data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Guang000/Awesome-Dataset-Distillation
공식 코드https://github.com/JiahaoWuGit/DConRec
공식 코드https://github.com/SaharR1372/DM_Style_matching
공식 코드https://github.com/VICO-UoE/DatasetCondensation
공식 코드https://github.com/VILA-Lab/SRe2L
공식 코드Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
https://github.com/algebraicdianuj/DC_U
공식 코드https://github.com/amazon-research/doscond
공식 코드https://github.com/backpropagator/bpc-cd
공식 코드https://github.com/hansong-zhang/dance
공식 코드DANCE: Dual-View Distribution Alignment for Dataset Condensation
https://github.com/hansong-zhang/m3d
공식 코드M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
https://github.com/he-y/multisize-dataset-condensation
공식 코드https://github.com/he-y/you-only-condense-once
공식 코드You Only Condense Once: Two Rules for Pruning Condensed Datasets
https://github.com/justincui03/dc_benchmark
공식 코드https://github.com/kaiwang960112/cafe
공식 코드https://github.com/khu-agi/hmdc
공식 코드Towards Model-Agnostic Dataset Condensation by Heterogeneous Models
https://github.com/mcg-nju/video-dc
공식 코드https://github.com/rafadd/condtsf
공식 코드CondTSF: One-line Plugin of Dataset Condensation for Time Series Forecasting
https://github.com/ringbdstack/bimsgc
공식 코드Bi-Directional Multi-Scale Graph Dataset Condensation via Information Bottleneck
https://github.com/saehyung-lee/dcc
공식 코드https://github.com/saintslab/pytoch
공식 코드https://github.com/shaoshitong/EDC
공식 코드https://github.com/shaoshitong/G_VBSM_Dataset_Condensation
공식 코드Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching
https://github.com/snu-mllab/efficient-dataset-condensation
공식 코드Dataset Condensation via Efficient Synthetic-Data Parameterization
https://github.com/uitrbn/idm
공식 코드https://github.com/vico-uoe/it-gan
공식 코드https://github.com/wanglin0126/gcsntk
공식 코드Fast Graph Condensation with Structure-based Neural Tangent Kernel
https://github.com/yolky/rfad
공식 코드Efficient Dataset Distillation Using Random Feature Approximation
https://github.com/zhyliu00/TimeSeriesCond
공식 코드Dataset Condensation for Time Series Classification via Dual Domain Matching
https://github.com/Guang000/Awesome-Dataset-Distillation/blob/main/README.md
https://github.com/PatrickZH/Improved-Deep-Leakage-from-Gradients
https://github.com/chandlerbang/gcond
https://github.com/he-y/soft-label-pruning-for-dataset-distillation
Generalized Large-Scale Data Condensation via Various Backbone and Statistical Matching
https://github.com/liuyugeng/baadd
https://github.com/miv-xjtu/speed
https://github.com/yuz1wan/video_distillation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.