# Exploring the Unexplored: Understanding the Impact of Layer Adjustments on Image Classification

Source: [Exploring the Unexplored: Understanding the Impact of Layer Adjustments on Image Classification](<https://www.sota2.com/research/paper/68fa71f11e209131cea53ab8>)

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

Data updated: 2025-10-26T02:15:27.440Z

Authors: Haixia Liu, Tim Brailsford, James Goulding, Gavin Smith, Larry Bull

Published: 2024-01-25T15:21:53.000Z

## Abstract

This paper investigates how adjustments to deep learning architectures impact model performance in image classification. Small-scale experiments generate initial insights although the trends observed are not consistent with the entire dataset. Filtering operations in the image processing pipeline are crucial, with image filtering before pre-processing yielding better results. The choice and order of layers as well as filter placement significantly impact model performance. This study provides valuable insights into optimizing deep learning models, with potential avenues for future research including collaborative platforms.

## 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/2401.14236>)

[PDF](<https://arxiv.org/pdf/2401.14236.pdf>)