Upcycling Models Under Domain and Category Shift (2023-03-13T00:00:00.000000Z)

TL;DR

This paper designs a novel, adaptive one-vs-all global clustering algorithm to achieve the distinction across different target classes and introduces a local k-NN clustering strategy to alleviate negative transfer.

Abstract

Deep neural networks (DNNs) often perform poorly in the presence of domain shift and category shift. How to upcycle DNNs and adapt them to the target task remains an important open problem. Unsupervised Domain Adaptation (UDA), especially recently proposed Source-free Domain Adaptation (SFDA), has become a promising technology to address this issue. Nevertheless, existing SFDA methods require that the source domain and target domain share the same label space, consequently being only applicable to the vanilla closed-set setting. In this paper, we take one step further and explore the Source-free Universal Domain Adaptation (SF-UniDA). The goal is to identify “known” data samples under both domain and category shift, and reject those “unknown” data samples (not present in source classes), with only the knowledge from standard pre-trained source model. To this end, we introduce an innovative global and local clustering learning technique (GLC). Specifically, we design a novel, adaptive one-vs-all global clustering algorithm to achieve the distinction across different target classes and introduce a local k-NN clustering strategy to alleviate negative transfer. 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. Remarkably, in the most challenging open-partial-set DA scenario, GLC outperforms UMAD by 14.8% on the VisDA benchmark. The code is available at https://github.com/ispc-lab/GLC.

Authors

Changjun Jiang

5 papers

Guang-Sheng Chen

2 papers

Sanqing Qu

6 papers

TL;DR

Abstract

Authors

References66 items

PSDC: A Prototype-Based Shared-Dummy Classifier Model for Open-Set Domain Adaptation

Geometric Anchor Correspondence Mining with Uncertainty Modeling for Universal Domain Adaptation

BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain Adaptation

scJoint integrates atlas-scale single-cell RNA-seq and ATAC-seq data with transfer learning

UMAD: Universal Model Adaptation under Domain and Category Shift

Generative Adversarial Networks

Generalized Source-free Domain Adaptation

Domain Consensus Clustering for Universal Domain Adaptation

Iterative human and automated identification of wildlife images

OVANet: One-vs-All Network for Universal Domain Adaptation

Source Data-Absent Unsupervised Domain Adaptation Through Hypothesis Transfer and Labeling Transfer

On the Effectiveness of Image Rotation for Open Set Domain Adaptation

Model Adaptation: Unsupervised Domain Adaptation Without Source Data

Universal Source-Free Domain Adaptation

A Balanced and Uncertainty-aware Approach for Partial Domain Adaptation

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

Universal Domain Adaptation through Self Supervision

The EU General Data Protection Regulation (GDPR)

Universal Domain Adaptation

Transferrable Prototypical Networks for Unsupervised Domain Adaptation

Unsupervised Open Domain Recognition by Semantic Discrepancy Minimization

Bridging Theory and Algorithm for Domain Adaptation

Learning to Transfer Examples for Partial Domain Adaptation

Integrative single-cell analysis

Contrastive Adaptation Network for Unsupervised Domain Adaptation

Moment Matching for Multi-Source Domain Adaptation

Partial Adversarial Domain Adaptation

Approach and practice: integrating earth observation resources for data sharing in China GEOSS

A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility

Open Set Domain Adaptation by Backpropagation

Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

Image to Image Translation for Domain Adaptation

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

VisDA: The Visual Domain Adaptation Challenge

Open Set Domain Adaptation

The EU General Data Protection Regulation (GDPR)

Partial Transfer Learning with Selective Adversarial Networks

Deep Hashing Network for Unsupervised Domain Adaptation

PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval

Conditional Adversarial Domain Adaptation

Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning

Zero-Shot Learning — The Good, the Bad and the Ugly

Remote Sensing Image Scene Classification: Benchmark and State of the Art

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Domain Separation Networks

Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation

Deep Residual Learning for Image Recognition

Domain-Adversarial Training of Neural Networks

Optimal Transport for Domain Adaptation

Transfer Feature Learning with Joint Distribution Adaptation

A Survey on Transfer Learning

Adapting Visual Category Models to New Domains

ImageNet: A large-scale hierarchical image database

Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

A Cluster Separation Measure

Progress and Challenges in Intelligent Remote Sensing Satellite Systems

Authorized licensed use limited to the terms of the applicable license agreement with IEEE. Restrictions apply. centric dynamic prototype strategy for source-free domain adaptation

Supplementary : Learning to Detect Open Classes for Universal Domain Adaptation

Single-cell transcriptomics of 20 mouse organs creates a tabula muris: The tabula muris consortium

Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks

A Mathematical Theory of Communication

Estimating the number of clusters in a dataset via the gap statistic

A dendrite method for cluster analysis

Some methods for classification and analysis of multivariate observations

Some methods for classi cation and analysis of multivariate observations

Bmd: A general class-balanced multi-20027

Field of Study

Journal Information

Name

Page

Venue Information

Name

Type

URL

Alternate Names