Unsupervised Domain Adaptation with Deep Neural-Network

Artem Bituitskii
7/10/2023
cs.CVcs.LG

No confident code implementation found for this paper. 5 possible matches can be shown below.

Abstract

This report contributes to the field of unsupervised domain adaptation by providing an analysis of existing methods, introducing a new approach, and demonstrating the potential for improving visual recognition tasks across different domains. The results of this study open up opportunities for further study and development of advanced methods in the field of domain adaptation.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

IDAL: Improved Domain Adaptive Learning for Natural Images Dataset

84% match2025

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

84% match2023

Vision Transformer-based Adversarial Domain Adaptation

83% match1 repo72024

OSSA: Unsupervised One-Shot Style Adaptation

83% match1 repo252024

Unsupervised Domain Adaptation for Training Event-Based Networks Using Contrastive Learning and Uncorrelated Conditioning

82% match2023

Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation

82% match1 repo122024

Cite this paper

@article{bituitskii2023unsupervised,
  title  = {Unsupervised Domain Adaptation with Deep Neural-Network},
  author = {Artem Bituitskii},
  year   = {2023},
  eprint = {2307.05601},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.05601v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2307.05601.svg)](https://codeofpaper.com/paper/2307.05601)

Discussion