Categories: FAANG

Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer

This paper was accepted at the workshop “Self-Supervised Learning – Theory and Practice” at NeurIPS 2022.
Self-supervised representation learning (SSL) methods provide an effective label-free initial condition for fine-tuning downstream tasks. However, in numerous realistic scenarios, the downstream task might be biased with respect to the target label distribution. This in turn moves the learned fine-tuned model posterior away from the initial (label) bias-free self-supervised model posterior. In this work, we re-interpret SSL fine-tuning under the lens of Bayesian continual learning and…

Self-Supervised Learning with Gaussian Processes

Self supervised learning (SSL) is a machine learning paradigm where models learn to understand the underlying structure of data without explicit supervision from labeled samples. The acquired representations from SSL have demonstrated useful for many downstream tasks including clustering, and linear classification, etc. To ensure smoothness of the representation space,…

January 31, 2026

In "FAANG"

Can You Remove the Downstream Model for Speaker Recognition with Self-Supervised Speech Features?

Self-supervised features are typically used in place of filter-bank features in speaker verification models. However, these models were originally designed to ingest filter-banks as inputs, and thus, training them on self-supervised features assumes that both feature types require the same amount of learning for the task. In this work, we…

August 20, 2024

In "FAANG"