Conditional Information Gain Trellis

Ufuk Can Biçici, Tuna Han Salih Meral, Lale Akarun

Abstract: Highlights•We introduce Conditional Information Gain Trellis (CIGT) for conditional computing.•We derive the CIGT loss function based on classification and information gain losses.•CIGT performs better or comparably using a fraction of the computational resources.•We give tests on MNIST, Fashion MNIST, and CIFAR 10, showing CIGT compares favorably.•Supplementary materials show that semantically similar classes are grouped together.