Abstract: Self-supervised learning allows AI systems to learn effective representations from large amounts of data using tasks that do not require costly labeling. Mode collapse, i.e., the model producing identical representations for all inputs, is a central problem to many self-supervised learning approaches, making self-supervised tasks, such as matching distorted variants of the inputs, ineffective. In this article, we argue that a straightforward application of information maximization among alternative latent representations of the same input naturally solves the collapse problem and achieves competitive empirical results. We propose a self-supervised learning method, CorInfoMax, that uses a second-order statistics-based mutual information measure that reflects the level of correlation among its arguments. Maximizing this correlative information measure between alternative representations of the same input serves two purposes: (1) it avoids the collapse problem by generating feature vectors with non-degenerate covariances; (2) it establishes relevance among alternative representations by increasing the linear dependence among them. An approximation of the proposed information maximization objective simplifies to a Euclidean distance-based objective function regularized by the log-determinant of the feature covariance matrix. The regularization term acts as a natural barrier against feature space degeneracy. Consequently, beyond avoiding complete output collapse to a single point, the proposed approach also prevents dimensional collapse by encouraging the spread of information across the whole feature space. Numerical experiments demonstrate that CorInfoMax achieves better or competitive performance results relative to the state-of-the-art SSL approaches.
 | Current position: PhD Student, Koç University (LinkedIn, Email) MS Thesis: Unsupervised learning of morphology. September 2022. (PDF, Presentation) |
Unsupervised learning of morphological rules is one of the expected abilities of natural language processing (NLP) models since children learn these rules during their native language acquisition without supervision. Based on this expectation, we present a comprehensive experimental setup for evaluating the morphological learning of several unsupervised models such as Autoencoders (AE), Variational Autoencoders (VAE), Character-level Language Models (CharLM) and Vector Quantized Variational Autoencoders (VQVAE) at the following tasks: probing for morphological features, morphological segmentation and morphological reinflection. In our study, we show that for probing, all models outperform baselines with an indication of encoding morphological knowledge; for morphological segmentation, VAE and CharLMs have comparable performances to unsupervised SOTA models; for morphological reinflection, VQVAE with multiple codebooks has the ability to identify the lemma and suffixes of a word and turns out to be a good candidate to perform inflectional tasks.
 | Current position: Senior Specialist - Data Science in Arçelik Global (LinkedIn) MS Thesis: Self-Supervised Learning with an Information Maximization Criterion. August 2022. (PDF, Presentation) |
Self-supervised learning provides a solution to learn effective representations from large amounts of data without performing data labeling, which is often expensive in terms of time, effort, and cost.The main problem with the self-supervised learning approach, in general, is collapse, i.e., obtaining identical representations for all inputs while matching different representations generated from the same input. In this thesis, we argue that information maximization among latent representations of different versions of the same input naturally prevents collapse. To this end, we propose a novel self-supervised learning method, CorInfoMax, based on maximizing the second-order statistics-based measure of mutual information that reflects the degree of correlation between the latent representation arguments. Maximizing this correlative information measure between alternative latent representations of the same input serves two main purposes: (1) it avoids the collapse problem by generating feature vectors with non-degenerate covariances; (2) it increases the linear dependence between alternative representations, ensuring that they are related to each other. The proposed information maximization objective is simplified to an objective function based on Euclidean distance regularized by the log-determinant of the feature covariance matrix. Due to the regularization term acting as a natural barrier against feature space degeneracy, CorInfoMax also prevents dimensional collapse by enforcing representations to span across the entire feature space. Empirical experiments show that CorInfoMax achieves better or competitive performance results over state-of-the-art self-supervised learning methods across different tasks and datasets.
