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On the limits of cross-domain generalization in automated X-ray prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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.
On the limits of cross-domain generalization in automated X-ray prediction
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Diverse Image-to-Image Translation via Disentangled Representations
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining
XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings
Very Deep Convolutional Networks for Large-Scale Image Recognition
SuperPoint: Self-Supervised Interest Point Detection and Description
Joint Contrastive Learning for Unsupervised Domain Adaptation
Rethinking Ensemble-Distillation for Semantic Segmentation Based Unsupervised Domain Adaptation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.