Unveiling the Potential of Probabilistic Embeddings in Self-Supervised Learning
This work addresses theoretical inconsistencies in self-supervised learning for researchers, but appears incremental as it builds on existing information-theoretic frameworks.
The paper investigated how probabilistic embeddings affect self-supervised learning by modeling representations stochastically and analyzing their impact on performance, information compression, and out-of-distribution detection. They found that constraining representation versus loss spaces differently affects performance, and adding a bottleneck in loss space significantly improves out-of-distribution detection.
In recent years, self-supervised learning has played a pivotal role in advancing machine learning by allowing models to acquire meaningful representations from unlabeled data. An intriguing research avenue involves developing self-supervised models within an information-theoretic framework, but many studies often deviate from the stochasticity assumptions made when deriving their objectives. To gain deeper insights into this issue, we propose to explicitly model the representation with stochastic embeddings and assess their effects on performance, information compression and potential for out-of-distribution detection. From an information-theoretic perspective, we seek to investigate the impact of probabilistic modeling on the information bottleneck, shedding light on a trade-off between compression and preservation of information in both representation and loss space. Emphasizing the importance of distinguishing between these two spaces, we demonstrate how constraining one can affect the other, potentially leading to performance degradation. Moreover, our findings suggest that introducing an additional bottleneck in the loss space can significantly enhance the ability to detect out-of-distribution examples, only leveraging either representation features or the variance of their underlying distribution.