Source-Free Domain Adaptation

57 papers with code • 3 benchmarks • 3 datasets

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Most implemented papers

Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain Adaptation

val-iisc/stickerda 27 Jul 2022

The prime challenge in unsupervised domain adaptation (DA) is to mitigate the domain shift between the source and target domains.

Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation

tim-learn/SHOT ICML 2020

Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain.

Tent: Fully Test-time Adaptation by Entropy Minimization

DequanWang/tent ICLR 2021

A model must adapt itself to generalize to new and different data during testing.

Casting a BAIT for Offline and Online Source-free Domain Adaptation

Albert0147/BAIT_SFUDA 23 Oct 2020

When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features.

Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation

albert0147/sfda_neighbors NeurIPS 2021

In this paper, we address the challenging source-free domain adaptation (SFDA) problem, where the source pretrained model is adapted to the target domain in the absence of source data.

ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation

yuhed/proxymix 29 May 2022

First of all, to avoid additional parameters and explore the information in the source model, ProxyMix defines the weights of the classifier as the class prototypes and then constructs a class-balanced proxy source domain by the nearest neighbors of the prototypes to bridge the unseen source domain and the target domain.

Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning

zyezhang/dac 12 Nov 2022

We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to the source data.

Upcycling Models under Domain and Category Shift

ispc-lab/glc CVPR 2023

We examine the superiority of our GLC on multiple benchmarks with different category shift scenarios, including partial-set, open-set, and open-partial-set DA.

Universal Source-Free Domain Adaptation

val-iisc/usfda CVPR 2020

1) In the Procurement stage, we aim to equip the model for future source-free deployment, assuming no prior knowledge of the upcoming category-gap and domain-shift.