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arXiv · 2110.05324

Learnable Adaptive Cosine Estimator (LACE) for Image Classification

Joshua Peeples·Connor McCurley·Sarah Walker·Dylan Stewart·Alina Zare
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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: https://github.com/GatorSense/LACE.

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Joshua Peeples, Connor McCurley, Sarah Walker, Dylan Stewart, Alina Zare. 2021-11-23. Learnable Adaptive Cosine Estimator (LACE) for Image Classification. https://doi.org/10.1109/wacv51458.2022.00381

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