# DSReLU: A Novel Dynamic Slope Function for Superior Model Training

Source: [DSReLU: A Novel Dynamic Slope Function for Superior Model Training](<https://www.sota2.com/research/paper/68fa747f1e209131cea61f62>)

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

Data updated: 2025-10-26T03:10:37.944Z

Authors: Archisman Chakraborti, Bidyut B Chaudhuri

Published: 2024-08-17T10:01:30.000Z

## Abstract

This study introduces a novel activation function, characterized by a dynamic slope that adjusts throughout the training process, aimed at enhancing adaptability and performance in deep neural networks for computer vision tasks. The rationale behind this approach is to overcome limitations associated with traditional activation functions, such as ReLU, by providing a more flexible mechanism that can adapt to different stages of the learning process. Evaluated on the Mini-ImageNet, CIFAR-100, and MIT-BIH datasets, our method demonstrated improvements in classification metrics and generalization capabilities. These results suggest that our dynamic slope activation function could offer a new tool for improving the performance of deep learning models in various image recognition tasks.

## 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/2408.09156>)

[PDF](<https://arxiv.org/pdf/2408.09156.pdf>)