Q-Match: Self-Supervised Learning by Matching Distributions Induced by a Queue

In semi-supervised learning, student-teacher distribution matching has been successful in improving performance of models using unlabeled data in conjunction with few labeled samples. In this paper, we aim to replicate that success in the self-supervised setup where we do not have access to any labeled data during pre-training. We introduce our algorithm, Q-Match, and show it is possible to induce the student-teacher distributions without any knowledge of downstream classes by using a queue of embeddings of samples from the unlabeled dataset. We focus our study on tabular datasets and show that Q-Match outperforms previous self-supervised learning techniques when measuring downstream classification performance. Furthermore, we show that our method is sample efficient-in terms of both the labels required for downstream training and the amount of unlabeled data required for pre-training-and scales well to the sizes of both the labeled and unlabeled data. Tabular data is the most common form of data for problems in industry. While many robust techniques exist to solve real-world machine learning problems on tabular data, most of these techniques require access to labels. Leveraging unlabeled data to learn good representations remains a key open problem in the tabular domain. In this work, we propose a flexible and powerful framework using deep learning that helps us use unlabeled data in the tabular domain. Deep learning has been successful in processing data in many different domains like images, audio, and text.