# Energy-Based Spherical Sparse Coding

Source: [Energy-Based Spherical Sparse Coding](<https://www.sota2.com/research/paper/68fa63ba1e209131cea006fa>)

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

Data updated: 2025-10-28T13:56:34.450Z

Authors: Bailey Kong, Charless C. Fowlkes

Published: 2017-10-04T22:22:50.000Z

## Abstract

In this paper, we explore an efficient variant of convolutional sparse coding with unit norm code vectors where reconstruction quality is evaluated using an inner product (cosine distance). To use these codes for discriminative classification, we describe a model we term Energy-Based Spherical Sparse Coding (EB-SSC) in which the hypothesized class label introduces a learned linear bias into the coding step. We evaluate and visualize performance of stacking this encoder to make a deep layered model for image classification.

## 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/1710.01820>)

[PDF](<https://arxiv.org/pdf/1710.01820.pdf>)