Towards Robust and Reproducible Active Learning Using Neural Networks (2002.09564v3)

Abstract: Active learning (AL) is a promising ML paradigm that has the potential to parse through large unlabeled data and help reduce annotation cost in domains where labeling data can be prohibitive. Recently proposed neural network based AL methods use different heuristics to accomplish this goal. In this study, we demonstrate that under identical experimental settings, different types of AL algorithms (uncertainty based, diversity based, and committee based) produce an inconsistent gain over random sampling baseline. Through a variety of experiments, controlling for sources of stochasticity, we show that variance in performance metrics achieved by AL algorithms can lead to results that are not consistent with the previously reported results. We also found that under strong regularization, AL methods show marginal or no advantage over the random sampling baseline under a variety of experimental conditions. Finally, we conclude with a set of recommendations on how to assess the results using a new AL algorithm to ensure results are reproducible and robust under changes in experimental conditions. We share our codes to facilitate AL evaluations. We believe our findings and recommendations will help advance reproducible research in AL using neural networks. We open source our code at https://github.com/PrateekMunjal/TorchAL

An Expert Overview of "Towards Robust and Reproducible Active Learning using Neural Networks"

The paper "Towards Robust and Reproducible Active Learning using Neural Networks" explores the landscape of Active Learning (AL) methods, particularly in the context of image classification, and critiques their efficacy, reproducibility, and robustness. The authors investigate the performance variance caused by experimental settings and evaluate the impact of advanced regularization techniques in improving baseline performances in AL.

Performance Variability Among AL Methods

The paper begins by examining inconsistencies across various AL methods. By implementing a wide range of experiments, the authors affirm that contemporary AL algorithms, such as uncertainty-based, diversity-based, and committee-based approaches, do not consistently outperform the simple Random Sampling Baseline (RSB). Notably, the authors observe that performance differences are smaller than what has been previously reported in the literature. The authors attribute these discrepancies to under-reporting of robust baseline performances in earlier studies and to variations in hyper-parameter tuning, architecture choice, and experimental setups. This has significant implications, suggesting that some of the purported gains in AL may be overstated.

Statistical Implications and Treatment of Variability

The paper provides a comprehensive statistical analysis, revealing that stochastic elements inherent to neural network training, such as initialization and mini-batch selection, cause significant variance in AL experiments. Consequently, this variance muddles performance comparisons among different AL methods. The authors utilize non-parametric statistical tests to further support their findings, suggesting that no single AL method consistently surpasses others or RSB across different data partitions and architectures. This underscores the need for a more standardized approach in benchmarking AL methods.

Regularization as a Mitigation Strategy

Addressing the issue of variance, the authors highlight the role of strong regularization techniques, such as Random Augmentation (RA) and Stochastic Weight Averaging (SWA), in enhancing model performance and reducing variance. Their experiments indicate that models fortified with these techniques exhibit significant improvements, achieving lower generalization error even with reduced training data. Importantly, these well-regularized models also diminish the performance gap with AL methods, questioning the added value of complex AL algorithms when strong baselines are configured optimally.

Transferability and Future Recommendations

Exploring transferability, the paper demonstrates that the performance gains achieved with one model architecture may not translate to others, contradicting some general assumptions in current AL literature. This observation prompts a call for transferability experiments to be a standard part of reporting AL results.

The paper concludes with recommendations to ensure the robustness and reproducibility of AL research. Suggestions include the use of robust baselines, incorporation of comprehensive transferability evaluations, consistent tuning of hyper-parameters across all AL iterations, and standardization of experimental methodologies. By providing a PyTorch-based toolkit alongside their research, the authors facilitate transparent and replicable evaluations of AL methods, aiming to standardize future investigations.

Implications and Speculations

The implications of this paper are significant for both the theory and practice of AL in neural networks. By advocating for improved benchmarks and methodologies, the paper calls into question past conclusions drawn about the relative efficacy of various AL methods. The research suggests a shift towards evaluating simpler, well-tuned baselines as potential viable competitors to complex AL strategies. Moving forward, the paper's recommendations could help in reorienting the research focus in AL towards more practical and reproducible studies, ultimately advancing the reliability and deployment of AL in various domains.