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New method uses random labels to study memorization in deep neural networks

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel method using random label prediction heads (RLP-heads) to empirically study memorization in deep neural networks. These RLP-heads, attached at various network depths, predict random labels from intermediate representations, offering a direct measure of sample-level memorization and model capacity by estimating Rademacher complexity. The study also introduces a new regularization technique based on RLP-head output to reduce memorization, finding that this reduction can impact generalization in dataset- and setup-dependent ways, challenging the direct equivalence of overfitting and memorization. AI

IMPACT This research offers new tools for understanding and potentially mitigating memorization in neural networks, which could lead to improved generalization in AI models.

RANK_REASON The cluster contains an academic paper detailing a new research methodology and findings in deep learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method uses random labels to study memorization in deep neural networks

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Marlon Becker, Jonas Konrad, Luis Garcia Rodriguez, Benjamin Risse ·

    Random Label Prediction Heads for Studying Memorization in Deep Neural Networks

    arXiv:2607.11541v1 Announce Type: new Abstract: We introduce a straightforward yet effective method to empirically study memorization in deep neural networks for classification tasks. Our approach augments each training sample with auxiliary random labels, which are then predicte…

  2. arXiv cs.LG TIER_1 English(EN) · Benjamin Risse ·

    Random Label Prediction Heads for Studying Memorization in Deep Neural Networks

    We introduce a straightforward yet effective method to empirically study memorization in deep neural networks for classification tasks. Our approach augments each training sample with auxiliary random labels, which are then predicted by a random label prediction head (RLP-head). …

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