Uncovering the Latent Potential of Deep Intermediate Representations
The paper titled 'Uncovering the Latent Potential of Deep Intermediate Representations' explores the distribution of task-relevant information across different layers of deep learning models. It introduces a method called Layer-wise Optimal Embedding Selection (LOES) to identify task-discriminative subspaces and proposes a Geometric Regularization Loss (GeoReg) to stabilize representation geometry during fine-tuning. The findings indicate that understanding layerwise embedding geometry is crucial for effective knowledge transfer in deep models.
- ▪Foundational models learn representations that evolve across depth, forming a hierarchy of embeddings.
- ▪Task-relevant information is distributed non-monotonically across layers and cannot be recovered by naïve aggregation.
- ▪LOES consistently outperforms standard baselines across various architectures and data regimes.
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Computer Science > Machine Learning arXiv:2605.23033 (cs) [Submitted on 21 May 2026] Title:Uncovering the Latent Potential of Deep Intermediate Representations Authors:Arnesh Batra, Arush Gumber, Aniket Khandelwal, Jashn Khemani, Anubha Gupta View a PDF of the paper titled Uncovering the Latent Potential of Deep Intermediate Representations, by Arnesh Batra and 4 other authors View PDF HTML (experimental) Abstract:Foundational Models pretrained on huge amount of data learn representations that evolve across depth, forming a hierarchy of embeddings with distinct semantic content and geometric structure.
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