Learning Discrete Representations via Information Maximizing Self-Augmented Training

Abstract: Learning discrete representations of data is a central machine learning task because of the compactness of the representations and ease of interpretation. The task includes clustering and hash learning as special cases. Deep neural networks are promising to be used because they can model the non-linearity of data and scale to large datasets. However, their model complexity is huge, and therefore, we need to carefully regularize the networks in order to learn useful representations that exhibit intended invariance for applications of interest. To this end, we propose a method called Information Maximizing Self-Augmented Training (IMSAT). In IMSAT, we use data augmentation to impose the invariance on discrete representations. More specifically, we encourage the predicted representations of augmented data points to be close to those of the original data points in an end-to-end fashion. At the same time, we maximize the information-theoretic dependency between data and their predicted discrete representations. Extensive experiments on benchmark datasets show that IMSAT produces state-of-the-art results for both clustering and unsupervised hash learning.

• The paper introduces IMSAT, a novel method that employs self-augmented training and mutual information maximization to learn compact, discrete representations.
• IMSAT leverages deep neural networks with data augmentation to enforce invariant representations, enhancing clustering and hash learning tasks.
• Experimental results show IMSAT outperforming traditional methods like K-means, DEC, and Deep RIM across multiple benchmark datasets.

Analyzing "Learning Discrete Representations via Information Maximizing Self-Augmented Training"

The paper "Learning Discrete Representations via Information Maximizing Self-Augmented Training (IMSAT)" addresses a significant problem in machine learning: the development of compact, discrete representations that are interpretable and useful for various applications, specifically clustering and hash learning. Discrete representations have the advantage of reducing the complexity of data interpretation while maintaining utility for tasks such as large-scale information retrieval and cluster analysis.

Core Methodology

IMSAT leverages deep neural networks for their capability to model the non-linear structures inherent in data while maintaining scalability to large datasets. The method introduces an innovative approach by utilizing an information-theoretic framework combined with a novel regularization technique, known as Self-Augmented Training (SAT).

The SAT method employs data augmentation to enforce invariance in learned representations. More specifically, it encourages the representations of augmented data to closely align with their original counterparts. Concurrently, IMSAT maximizes the mutual information between the input data and their discrete representations to ensure the preservation of informative features.

Experimental Evaluation

The effectiveness of IMSAT is demonstrated through comprehensive experiments on multiple benchmark datasets including MNIST, Omniglot, STL, CIFAR10, CIFAR100, SVHN, Reuters, and 20news. IMSAT consistently outperformed traditional methods like $K$-means and other competing approaches such as DEC and Deep RIM across various datasets.

Particular attention is given to the clustering task on the Omniglot dataset, where IMSAT achieves significant improvements by incorporating invariant transformations specific to the dataset, like affine distortions, showcasing the method's flexibility and adaptability. In hash learning, IMSAT demonstrates superior performance by utilizing larger neural network architectures to faithfully model complex data distributions.

Implications and Future Directions

The implications of IMSAT are multifaceted. Theoretically, it advances the understanding of unsupervised learning strategies by linking mutual information maximization with self-augmentative regularization. Practically, IMSAT offers a robust framework for discrete representation learning that can be adapted to various types of data, such as images and text, with minimal task-specific adjustments.

Looking forward, IMSAT's approach to regularization through data augmentation could inspire future research in unsupervised and semi-supervised learning domains. Applying this method to structured data, like graphs or sequences, presents a promising direction for future work, necessitating the design of novel augmentation strategies suited to these data types.

IMSAT's adaptability and performance highlight its potential as a pivotal method in the unsupervised learning toolkit, providing a model for building interpretable and efficient representations in complex machine learning tasks.