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Learning with Hierarchical Complement Objective

Overview

This codebase focus on "Explicitly Hierarchy" implemented in "Learning with Hierarchical Complement Objective" for anonymized code submission. Since the codebase of "Latent Hierarchy" involved many details of Semantic Segmentation, We will summarize it as soon as possible and open another repo to present.

Requires

  • Python 3.6
  • Pytorch 1.2.0
  • keras
  • tensorflow
  • numpy 1.16.4

Usage

For getting baseline results

python main.py --sess Baseline_session

For training via Complement objective

python main.py --COT --sess COT_session

For training via Hierarchical Complement Entropy (HCE)

python main.py --HCOT --sess HCOT_session

Our Benchmark on CIFAR100

The following table shows the test error rates in a 200-epoch training session. (Please refer to "Table 1" in the paper for details.)

Model Baseline COT HCOT
PreAct ResNet-18 25.44% 24.73% 23.8%