# Learnable Adaptive Cosine Estimator (LACE) for Image Classification

Source: [Learnable Adaptive Cosine Estimator (LACE) for Image Classification](<https://www.sota2.com/research/paper/68fa6bdf1e209131cea30be3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:59:05.045Z

Authors: Joshua Peeples, Connor McCurley, Sarah Walker, Dylan Stewart, Alina Zare

Published: 2021-10-11T14:45:15.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2110.05324>)

[PDF](<https://arxiv.org/pdf/2110.05324.pdf>)