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arXiv · 2408.12936

Smooth InfoMax -- Towards Easier Post-Hoc Interpretability

Fabian Denoodt·Bart de Boer·José Oramas
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Abstract

We introduce Smooth InfoMax (SIM), a self-supervised representation learning method that incorporates interpretability constraints into the latent representations at different depths of the network. Based on $\beta$-VAEs, SIM's architecture consists of probabilistic modules optimized locally with the InfoNCE loss to produce Gaussian-distributed representations regularized toward the standard normal distribution. This creates smooth, well-defined, and better-disentangled latent spaces, enabling easier post-hoc analysis. Evaluated on speech data, SIM preserves the large-scale training benefits of Greedy InfoMax while improving the effectiveness of post-hoc interpretability methods across layers.

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Fabian Denoodt, Bart de Boer, José Oramas. 2024-08-23. Smooth InfoMax -- Towards Easier Post-Hoc Interpretability. https://arxiv.org/abs/2408.12936

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