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arXiv · 2302.09649

Weakly Supervised Label Learning Flows

You Lu·Wenzhuo Song·Chidubem Arachie·Bert Huang
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Abstract

Supervised learning usually requires a large amount of labelled data. However, attaining ground-truth labels is costly for many tasks. Alternatively, weakly supervised methods learn with cheap weak signals that only approximately label some data. Many existing weakly supervised learning methods learn a deterministic function that estimates labels given the input data and weak signals. In this paper, we develop label learning flows (LLF), a general framework for weakly supervised learning problems. Our method is a generative model based on normalizing flows. The main idea of LLF is to optimize the conditional likelihoods of all possible labelings of the data within a constrained space defined by weak signals. We develop a training method for LLF that trains the conditional flow inversely and avoids estimating the labels. Once a model is trained, we can make predictions with a sampling algorithm. We apply LLF to three weakly supervised learning problems. Experiment results show that our method outperforms many baselines we compare against.

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You Lu, Wenzhuo Song, Chidubem Arachie, Bert Huang. 2023-02-19. Weakly Supervised Label Learning Flows. https://doi.org/10.1016/j.neunet.2024.106892

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