Measuring Déjà vu Memorization Efficiently

• URL: http://arxiv.org/abs/2504.05651v1
• Date: Tue, 08 Apr 2025 03:55:20 GMT
• Title: Measuring Déjà vu Memorization Efficiently
• Authors: Narine Kokhlikyan, Bargav Jayaraman, Florian Bordes, Chuan Guo, Kamalika Chaudhuri,
• Abstract summary: Recent research has shown that representation learning models may accidentally memorize their training data.<n>We propose alternative simple methods to estimate dataset-level correlations.<n>These can be used to approximate an off-the-shelf model's memorization ability without any retraining.
• Score: 38.201992966736114
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent research has shown that representation learning models may accidentally memorize their training data. For example, the d\'ej\`a vu method shows that for certain representation learning models and training images, it is sometimes possible to correctly predict the foreground label given only the representation of the background - better than through dataset-level correlations. However, their measurement method requires training two models - one to estimate dataset-level correlations and the other to estimate memorization. This multiple model setup becomes infeasible for large open-source models. In this work, we propose alternative simple methods to estimate dataset-level correlations, and show that these can be used to approximate an off-the-shelf model's memorization ability without any retraining. This enables, for the first time, the measurement of memorization in pre-trained open-source image representation and vision-language representation models. Our results show that different ways of measuring memorization yield very similar aggregate results. We also find that open-source models typically have lower aggregate memorization than similar models trained on a subset of the data. The code is available both for vision and vision language models.

Related papers

• A Geometric Framework for Understanding Memorization in Generative Models [11.263296715798374]
  Recent work has shown that deep generative models can be capable of memorizing and reproducing training datapoints when deployed. These findings call into question the usability of generative models, especially in light of the legal and privacy risks brought about by memorization. We propose the manifold memorization hypothesis (MMH), a geometric framework which leverages the manifold hypothesis into a clear language in which to reason about memorization.
  arXiv  Detail & Related papers  (2024-10-31T18:09:01Z)
• Extracting Training Data from Unconditional Diffusion Models [76.85077961718875]
  diffusion probabilistic models (DPMs) are being employed as mainstream models for generative artificial intelligence (AI) We aim to establish a theoretical understanding of memorization in DPMs with 1) a memorization metric for theoretical analysis, 2) an analysis of conditional memorization with informative and random labels, and 3) two better evaluation metrics for measuring memorization. Based on the theoretical analysis, we propose a novel data extraction method called textbfSurrogate condItional Data Extraction (SIDE) that leverages a trained on generated data as a surrogate condition to extract training data directly from unconditional diffusion models.
  arXiv  Detail & Related papers  (2024-06-18T16:20:12Z)
• Causal Estimation of Memorisation Profiles [58.20086589761273]
  Understanding memorisation in language models has practical and societal implications. Memorisation is the causal effect of training with an instance on the model's ability to predict that instance. This paper proposes a new, principled, and efficient method to estimate memorisation based on the difference-in-differences design from econometrics.
  arXiv  Detail & Related papers  (2024-06-06T17:59:09Z)
• Pre-Trained Vision-Language Models as Partial Annotators [40.89255396643592]
  Pre-trained vision-language models learn massive data to model unified representations of images and natural languages. In this paper, we investigate a novel "pre-trained annotating - weakly-supervised learning" paradigm for pre-trained model application and experiment on image classification tasks.
  arXiv  Detail & Related papers  (2024-05-23T17:17:27Z)
• Déjà Vu Memorization in Vision-Language Models [39.51189095703773]
  We propose a new method for measuring memorization in Vision-Language Models (VLMs) We show that the model indeed retains information about individual objects in the training images beyond what can be inferred from correlations or the image caption. We evaluate d'eja vu memorization at both sample and population level, and show that it is significant for OpenCLIP trained on as many as 50M image-caption pairs.
  arXiv  Detail & Related papers  (2024-02-03T09:55:35Z)
• Emergent and Predictable Memorization in Large Language Models [23.567027014457775]
  Memorization, or the tendency of large language models to output entire sequences from their training data verbatim, is a key concern for safely deploying language models. We seek to predict which sequences will be memorized before a large model's full train-time by extrapolating the memorization behavior of lower-compute trial runs. We provide further novel discoveries on the distribution of memorization scores across models and data.
  arXiv  Detail & Related papers  (2023-04-21T17:58:31Z)
• Debiasing Vision-Language Models via Biased Prompts [79.04467131711775]
  We propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding. We show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models.
  arXiv  Detail & Related papers  (2023-01-31T20:09:33Z)
• Learning to Model and Ignore Dataset Bias with Mixed Capacity Ensembles [66.15398165275926]
  We propose a method that can automatically detect and ignore dataset-specific patterns, which we call dataset biases. Our method trains a lower capacity model in an ensemble with a higher capacity model. We show improvement in all settings, including a 10 point gain on the visual question answering dataset.
  arXiv  Detail & Related papers  (2020-11-07T22:20:03Z)
• Automatic Recall Machines: Internal Replay, Continual Learning and the Brain [104.38824285741248]
  Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity. We present a method where these auxiliary samples are generated on the fly, given only the model that is being trained for the assessed objective. Instead the implicit memory of learned samples within the assessed model itself is exploited.
  arXiv  Detail & Related papers  (2020-06-22T15:07:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.