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Domain Adaptation

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보존된 평가 기록: 391

Domain Adaptation is the task of adapting models across domains. This is motivated by the challenge where the test and training datasets fall from different data distributions due to some factor. Domain adaptation aims to build machine learning models that can be generalized into a target domain and dealing with the discrepancy across domain distributions.

Further readings:

A Brief Review of Domain Adaptation

<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Adaptive Control Attention Network for Underwater Acoustic Localization and Domain Adaptation

Keeping Medical AI Healthy: A Review of Detection and Correction Methods for System Degradation

Collaborative Editable Model

One-Shot Neural Architecture Search with Network Similarity Directed Initialization for Pathological Image Classification

M3SD: Multi-modal, Multi-scenario and Multi-language Speaker Diarization Dataset

Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving Regularization

BUT System for the MLC-SLM Challenge

Hierarchical Multi-Positive Contrastive Learning for Patent Image Retrieval

How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection

Cross-Domain Conditional Diffusion Models for Time Series Imputation

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching

Exploring Audio Cues for Enhanced Test-Time Video Model Adaptation

crossMoDA Challenge: Evolution of Cross-Modality Domain Adaptation Techniques for Vestibular Schwannoma and Cochlea Segmentation from 2021 to 2023

SIMSHIFT: A Benchmark for Adapting Neural Surrogates to Distribution Shifts

Flick: Few Labels Text Classification using K-Aware Intermediate Learning in Multi-Task Low-Resource Languages

ConStyX: Content Style Augmentation for Generalizable Medical Image Segmentation

BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP

MUDAS: Mote-scale Unsupervised Domain Adaptation in Multi-label Sound Classification

BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection

SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation

Harmonizing and Merging Source Models for CLIP-based Domain Generalization

Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments

Leveraging Depth and Language for Open-Vocabulary Domain-Generalized Semantic Segmentation

Test-Time Adaptation for Generalizable Task Progress Estimation

RoCA: Robust Cross-Domain End-to-End Autonomous Driving

Low-resource domain adaptation while minimizing energy and hardware resource consumption

Integration of Old and New Knowledge for Generalized Intent Discovery: A Consistency-driven Prototype-Prompting Framework

FEDTAIL: Federated Long-Tailed Domain Generalization with Sharpness-Guided Gradient Matching

Adapting Vision-Language Foundation Model for Next Generation Medical Ultrasound Image Analysis

PlantBert: An Open Source Language Model for Plant Science

SUTA-LM: Bridging Test-Time Adaptation and Language Model Rescoring for Robust ASR

Flowing Datasets with Wasserstein over Wasserstein Gradient Flows

SurgBench: A Unified Large-Scale Benchmark for Surgical Video Analysis

Clustered Federated Learning via Embedding Distributions

Play to Generalize: Learning to Reason Through Game Play

Dealing with the Evil Twins: Improving Random Augmentation by Addressing Catastrophic Forgetting of Diverse Augmentations

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