mil-benchmarks: Standardized Evaluation of Deep Multiple-Instance Learning Techniques

Abstract

Multiple-instance learning is a subset of weakly supervised learning where labels are applied to sets of instances rather than the instances themselves. Under the standard assumption, a set is positive only there is if at least one instance in the set which is positive. This paper introduces a series of multiple-instance learning benchmarks generated from MNIST, Fashion-MNIST, and CIFAR10. These benchmarks test the standard, presence, absence, and complex assumptions and provide a framework for future benchmarks to be distributed. I implement and evaluate several multiple-instance learning techniques against the benchmarks. Further, I evaluate the Noisy-And method with label noise and find mixed results with different datasets. The models are implemented in TensorFlow 2.4.1 and are available on GitHub. The benchmarks are available from PyPi as mil-benchmarks and on GitHub.

References in corpus (6)

• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
• Matching Networks for One Shot Learning
• Multiple Instance Learning: A Survey of Problem Characteristics and Applications
• Audio Event Detection using Weakly Labeled Data
• Confidence-Constrained Maximum Entropy Framework for Learning from Multi-Instance Data