Domain Generalization
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The idea of Domain Generalization is to learn from one or multiple training domains, to extract a domain-agnostic model which can be applied to an unseen domain
Source: Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/suhyeonlee/wildnet
공식 코드WildNet: Learning Domain Generalized Semantic Segmentation from the Wild
https://github.com/sunnweiwei/user-satisfaction-simulation
공식 코드Simulating User Satisfaction for the Evaluation of Task-oriented Dialogue Systems
https://github.com/sunyiyou/safas
공식 코드Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
https://github.com/superz678/mg-had
공식 코드https://github.com/szc12153/sparse_meta_tuning
공식 코드Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts
https://github.com/tareknaous/readme
공식 코드ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment
https://github.com/tayebiarasteh/fldomain
공식 코드Mind the Gap: Federated Learning Broadens Domain Generalization in Diagnostic AI Models
https://github.com/tayebiarasteh/privacydomain
공식 코드https://github.com/taylover-pei/SSDG-CVPR2020
공식 코드https://github.com/tejas-gokhale/alt
공식 코드Improving Diversity with Adversarially Learned Transformations for Domain Generalization
https://github.com/tensorflow/models
공식 코드https://github.com/tensorflow/tpu
공식 코드https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet
공식 코드EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/thongnt99/acl22-depression-phq9
공식 코드Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
https://github.com/thuan2412/a-principled-approach-for-model-validation-for-domain-generalization
공식 코드A principled approach to model validation in domain generalization
https://github.com/thuan2412/conditional
공식 코드Conditional entropy minimization principle for learning domain invariant representation features
https://github.com/thuan2412/conditional_entropy_minimization_for_domain_generalization
공식 코드Conditional entropy minimization principle for learning domain invariant representation features
https://github.com/thuan2412/joint-covariate-alignment-and-concept-alignment-for-domain-generalization
공식 코드Joint covariate-alignment and concept-alignment: a framework for domain generalization
https://github.com/thuan2412/tradeoff_between_domain_alignment_and_reconstruction_loss
공식 코드Trade-off between reconstruction loss and feature alignment for domain generalization
https://github.com/thuml/videodg
공식 코드VideoDG: Generalizing Temporal Relations in Videos to Novel Domains
https://github.com/thunlp/deepperception
공식 코드https://github.com/tidbcloud/tiinsight
공식 코드Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models
https://github.com/tigrangalstyan/wilds
공식 코드https://github.com/tingz123/fha-kitchens
공식 코드https://github.com/tomsherborne/tram_optimizer
공식 코드TRAM: Bridging Trust Regions and Sharpness Aware Minimization
https://github.com/tongjingqi/code2logic
공식 코드Code2Logic: Game-Code-Driven Data Synthesis for Enhancing VLMs General Reasoning
https://github.com/toughstonex/mvs_evaluation_benchmark
공식 코드https://github.com/trinhvg/impash
공식 코드IMPaSh: A Novel Domain-shift Resistant Representation for Colorectal Cancer Tissue Classification
https://github.com/tum-vision/scenedino
공식 코드Feed-Forward SceneDINO for Unsupervised Semantic Scene Completion
https://github.com/tylfromseu/dpstyler
공식 코드DPStyler: Dynamic PromptStyler for Source-Free Domain Generalization
https://github.com/uds-lsv/llmft
공식 코드Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation
https://github.com/umeannever/b2ner
공식 코드https://github.com/uoguelph-mlrg/Cutout
공식 코드Improved Regularization of Convolutional Neural Networks with Cutout
https://github.com/ut-vision/FgSty-CPL
공식 코드https://github.com/ut-vision/rot-mvgaze
공식 코드Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.