Papers › Learnable Adaptive Cosine Estimator (LACE) for Image Classification
Learnable Adaptive Cosine Estimator (LACE) for Image Classification
Joshua Peeples, Connor McCurley, Sarah Walker, Dylan Stewart, Alina Zare
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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