https://github.com/anubhav4sachan/domain-adapt
Unsupervised Domain Adaptation for Cross-sensor Pore Detection in High-resolution Fingerprint Images
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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:
<span style="color:grey; opacity: 0.6">( Image credit: Unsupervised Image-to-Image Translation Networks )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Unsupervised Domain Adaptation for Cross-sensor Pore Detection in High-resolution Fingerprint Images
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation
Very Deep Convolutional Networks for Large-Scale Image Recognition
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Deep CORAL: Correlation Alignment for Deep Domain Adaptation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Long-range UAV Thermal Geo-localization with Satellite Imagery
Visual Representations for Semantic Target Driven Navigation
ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Learning from Simulated and Unsupervised Images through Adversarial Training
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.