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Orthogonality Loss: Learning Discriminative ...
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由 S Yang 著作2020被引用 29 次 — We propose Orthogonality loss as an elegant penalty item appends to common classification loss to learn the discriminative representations.
Orthogonality Loss: Learning Discriminative ...
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由 S Yang 著作2020被引用 29 次 — The main idea is that in order for weight vectors to be discriminative, it should be as close as possible to be orthogonal to each other in the vector space.
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Orthogonality Loss - Face Recognition
ResearchGate
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2024年12月9日 — To the end, we propose Orthogonality loss as an elegant penalty item appends to common classification loss to learn the discriminative ...
Learning Discriminative Representations for Face ...
Semantic Scholar
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2021年6月1日 — This article considers enlarging the inter-class variance by directly penalizing weight vectors of last fully connected layer, ...
Orthogonal Projection Loss
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由 K Ranasinghe 著作2021被引用 82 次 — The idea of orthogonality is also used for disentangled representations such as in [55] and to stabi- lize network training since orthogonalization ensures en-.
11 頁
Orthogonal Subspace Decomposition: A New Perspective ...
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由 J Wang 著作 — This paper proposes an orthogonal subspace decomposition (OSD) method for the face clustering task. The OSD module can learn more discriminative node features ...
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Mei Wang
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Fair loss: Margin-aware reinforcement learning for deep face recognition ... Orthogonality loss: Learning discriminative representations for face recognition.
Entropy and orthogonality based deep discriminative ...
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由 W Shi 著作2018被引用 17 次 — We propose a novel discriminative feature learning method of CNNs by enforcing the learned feature vectors to have class-selectivity.
Fairness by Learning Orthogonal Disentangled ...
European Computer Vision Association
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Compared to the baseline, we observe that adding entropy loss and orthogonality constraints on the representations lowers the discriminative power of the ...
16 頁
Sample-guided Contrastive Regularization for Deep Face ...
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由 Y Song 著作2024被引用 7 次 — To avoid image quality degradation, we augment the embeddings instead of the images in order to integrate contrastive learning into face recognition.
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