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Photo to Rest Generalization

Single-Source Domain Generalization › Photo to Rest Generalization 9 평가 기록 2 datasets

It is the practical scenario of training on set of easy-to-collect real photographs and evaluate on the rest of diverse-styled domains (art, cartoon, sketch). Photo-to-rest generalization is a special case of the single-source domain generalization (SSDG) task.

Using only real photographs for training is the only way for SSDG to be compatible with ImageNet pre-trained networks. For any other domain as source, access to the photo domain through ImageNet pre-training violates the hypothesis of the task of a single domain seen during training, taking the task closer to the multy-source domain generalization (MSDG). Finaly pre-training on imagenet, training on a non real photograph domain and testing on real photographs violates the SSDG hypothesis that the target domain should not be used during training.

A large part of the generalization community chooses only this task over SSDG for pre-trained networks and for datasets that contain the photo domain.

개요는 각 항목의 일부입니다. 위 탭에서 전체 기록을 페이지별로 탐색할 수 있습니다.

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Dataset기록된 모델 예시MetricPaperCode
Crafting-Shifts(ResNet18)
Accuracy: 65.85
논문 코드
Crafting-Shifts(ResNet18)
Accuracy: 57.35
논문 코드

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