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[bibtex]@InProceedings{Peeples_2022_WACV, author = {Peeples, Joshua and McCurley, Connor H. and Walker, Sarah and Stewart, Dylan and Zare, Alina}, title = {Learnable Adaptive Cosine Estimator (LACE) for Image Classification}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, month = {January}, year = {2022}, pages = {3479-3489} }
Learnable Adaptive Cosine Estimator (LACE) for Image Classification
Abstract
In this work, we propose a new loss to improve feature discriminability and classification performance. Motivated by the adaptive cosine/coherence estimator (ACE), our proposed method incorporates angular information that is inherently learned by artificial neural networks. Our learnable ACE (LACE) transforms the data into a new "whitened" space that improves the inter-class separability and intra-class compactness. We compare our LACE to alternative state-of-the art softmax-based and feature regularization approaches. Our results show that the proposed method can serve as a viable alternative to cross entropy and angular softmax approaches. Our code is publicly available.
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