Conditional Classification: A Solution for Computational Energy Reduction

TL;DR

This paper introduces a conditional classification method that reduces the computational complexity of deep CNNs by splitting the classification process into coarse and fine steps, activating only necessary model parts.

Contribution

It presents a novel two-step classification approach that maintains high accuracy while significantly lowering computational costs compared to traditional models.

Findings

Achieves comparable accuracy to state-of-the-art models

Reduces Flop Count by activating fewer model parts

Efficiently classifies images with less computation

Abstract

Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational complexity is growing exponentially. In this paper, we propose a novel solution to reduce the computational complexity of convolutional neural network models used for many class image classification. Our proposed technique breaks the classification task into two steps: 1) coarse-grain classification, in which the input samples are classified among a set of hyper-classes, 2) fine-grain classification, in which the final labels are predicted among those hyper-classes detected at the first step. We illustrate that our proposed classifier can reach the level of accuracy reported by the best in class classification models with less computational complexity (Flop Count) by only activating parts of the model that are needed for the image classification.