# CNNtention: Can CNNs do better with Attention?

Source: [CNNtention: Can CNNs do better with Attention?](<https://www.sota2.com/research/paper/68fa76741e209131cea6d181>)

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

Data updated: 2025-10-26T03:54:27.044Z

Authors: Nikhil Kapila, Julian Glattki, Tejas Rathi

Published: 2024-12-16T11:00:02.000Z

## Abstract

Convolutional Neural Networks (CNNs) have been the standard for image classification tasks for a long time, but more recently attention-based mechanisms have gained traction. This project aims to compare traditional CNNs with attention-augmented CNNs across an image classification task. By evaluating and comparing their performance, accuracy and computational efficiency, the project will highlight benefits and trade-off of the localized feature extraction of traditional CNNs and the global context capture in attention-augmented CNNs. By doing this, we can reveal further insights into their respective strengths and weaknesses, guide the selection of models based on specific application needs and ultimately, enhance understanding of these architectures in the deep learning community.   This was our final project for CS7643 Deep Learning course at Georgia Tech.

## 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/2412.11657>)

[PDF](<https://arxiv.org/pdf/2412.11657.pdf>)