arxiv:2302.12185

Scaling Up Computer Vision Neural Networks Using Fast Fourier Transform

Published on Feb 2, 2023

Authors:

Siddharth Agrawal

Abstract

Fast Fourier Transform is employed to address scaling challenges in Convolutional Neural Networks with larger kernels and Vision Transformers with quadratic complexity for high-resolution images.

AI-generated summary

Deep Learning-based Computer Vision field has recently been trying to explore larger kernels for convolution to effectively scale up Convolutional Neural Networks. Simultaneously, new paradigm of models such as Vision Transformers find it difficult to scale up to larger higher resolution images due to their quadratic complexity in terms of input sequence. In this report, Fast Fourier Transform is utilised in various ways to provide some solutions to these issues.

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