CTBENCH: A Library and Benchmark for Certified Training

TL;DR

CTBench is a unified library and benchmark for certifiably robust neural network training, enabling fair comparison, systematic tuning, and new insights into current methods and their limitations.

Contribution

The paper introduces CTBench, a comprehensive benchmark and library for certified training, standardizing evaluation and revealing new insights and state-of-the-art performance.

Findings

Most algorithms outperform previous literature significantly.

Fair training and hyperparameter tuning reduce claimed advantages of recent algorithms.

Certified models exhibit less fragmented loss surfaces, shared mistakes, and sparser activations.

Abstract

Training certifiably robust neural networks is an important but challenging task. While many algorithms for (deterministic) certified training have been proposed, they are often evaluated on different training schedules, certification methods, and systematically under-tuned hyperparameters, making it difficult to compare their performance. To address this challenge, we introduce CTBench, a unified library and a high-quality benchmark for certified training that evaluates all algorithms under fair settings and systematically tuned hyperparameters. We show that (1) almost all algorithms in CTBench surpass the corresponding reported performance in literature in the magnitude of algorithmic improvements, thus establishing new state-of-the-art, and (2) the claimed advantage of recent algorithms drops significantly when we enhance the outdated baselines with a fair training schedule, a fair certification method and well-tuned hyperparameters. Based on CTBench, we provide new insights into the current state of certified training, including (1) certified models have less fragmented loss surface, (2) certified models share many mistakes, (3) certified models have more sparse activations, (4) reducing regularization cleverly is crucial for certified training especially for large radii and (5) certified training has the potential to improve out-of-distribution generalization. We are confident that CTBench will serve as a benchmark and testbed for future research in certified training.