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홈 뉴스 최신 연구 논문 튜토리얼 데이터셋 백과사전 SOTA LLM 모델 GPU 랭킹 컨퍼런스

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Domain Adaptation On Usps To Mnist

평가 지표

Accuracy

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름	Accuracy	Paper Title	Repository
DRANet	97.8	DRANet: Disentangling Representation and Adaptation Networks for Unsupervised Cross-Domain Adaptation	-
DFA-MCD	96.6	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment	-
SRDA (RAN)	95.03	Learning Smooth Representation for Unsupervised Domain Adaptation	-
FAMCD	98.75	Unsupervised domain adaptation using feature aligned maximum classifier discrepancy	-
MCD	95.7	Maximum Classifier Discrepancy for Unsupervised Domain Adaptation	-
Mean teacher	98.07	Self-ensembling for visual domain adaptation	-
CDAN	98.0	Conditional Adversarial Domain Adaptation	-
DFA-ENT	96.2	Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment	-
MCD+CAT	96.3	Cluster Alignment with a Teacher for Unsupervised Domain Adaptation	-
CyCleGAN (Light-weight Calibrator)	98.3	Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation	-
FACT	98.6	FACT: Federated Adversarial Cross Training	-
SHOT	98.4	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation	-
3CATN	98.3	Cycle-consistent Conditional Adversarial Transfer Networks	-
DeepJDOT	96.4	DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation	-

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Domain Adaptation On Usps To Mnist | SOTA | HyperAI초신경