On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition

• URL: http://arxiv.org/abs/2106.03062v1
• Date: Sun, 6 Jun 2021 08:24:42 GMT
• Title: On Training Sample Memorization: Lessons from Benchmarking Generative Modeling with a Large-scale Competition
• Authors: Ching-Yuan Bai, Hsuan-Tien Lin, Colin Raffel, and Wendy Chih-wen Kan
• Abstract summary: In this work, we critically evaluate the gameability of metrics by designing and deploying a generative modeling competition. The competitiveness between participants allowed us to investigate both intentional and unintentional memorization in generative modeling. To detect intentional memorization, we propose the Memorization-Informed Fr'echet Inception Distance'' (MiFID) as a new memorization-aware metric.
• Score: 27.058164653689605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Many recent developments on generative models for natural images have relied on heuristically-motivated metrics that can be easily gamed by memorizing a small sample from the true distribution or training a model directly to improve the metric. In this work, we critically evaluate the gameability of these metrics by designing and deploying a generative modeling competition. Our competition received over 11000 submitted models. The competitiveness between participants allowed us to investigate both intentional and unintentional memorization in generative modeling. To detect intentional memorization, we propose the ``Memorization-Informed Fr\'echet Inception Distance'' (MiFID) as a new memorization-aware metric and design benchmark procedures to ensure that winning submissions made genuine improvements in perceptual quality. Furthermore, we manually inspect the code for the 1000 top-performing models to understand and label different forms of memorization. Our analysis reveals that unintentional memorization is a serious and common issue in popular generative models. The generated images and our memorization labels of those models as well as code to compute MiFID are released to facilitate future studies on benchmarking generative models.

Related papers

• GRAM: A Generative Foundation Reward Model for Reward Generalization [48.63394690265176]
  We develop a generative reward model that is first trained via large-scale unsupervised learning and then fine-tuned via supervised learning.<n>This model generalizes well across several tasks, including response ranking, reinforcement learning from human feedback, and task adaptation with fine-tuning.
  arXiv  Detail & Related papers  (2025-06-17T04:34:27Z)
• Redistribute Ensemble Training for Mitigating Memorization in Diffusion Models [31.92526915009259]
  Diffusion models are known for their tremendous ability to generate high-quality samples. Recent methods for memory mitigation have primarily addressed the issue within the context of the text modality. We propose a novel method for diffusion models from the perspective of visual modality, which is more generic and fundamental for mitigating memorization.
  arXiv  Detail & Related papers  (2025-02-13T15:56:44Z)
• 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)
• Embedding Space Selection for Detecting Memorization and Fingerprinting in Generative Models [45.83830252441126]
  Generative Adversarial Networks (GANs) and Diffusion Models have become cornerstone technologies, driving innovation in diverse fields from art creation to healthcare. Despite their potential, these models face the significant challenge of data memorization, which poses risks to privacy and the integrity of generated content. We study memorization scores calculated from encoder layer embeddings, which involves measuring distances between samples in the embedding spaces.
  arXiv  Detail & Related papers  (2024-07-30T19:52:49Z)
• 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)
• Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted [15.162296378581853]
  Large-scale text-to-image diffusion models excel in generating high-quality images from textual inputs. Concerns arise as research indicates their tendency to memorize and replicate training data. Efforts within the text-to-image community to address memorization explore causes such as data duplication, replicated captions, or trigger tokens.
  arXiv  Detail & Related papers  (2024-06-01T15:47:13Z)
• Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention [62.671435607043875]
  Research indicates that text-to-image diffusion models replicate images from their training data, raising tremendous concerns about potential copyright infringement and privacy risks. We reveal that during memorization, the cross-attention tends to focus disproportionately on the embeddings of specific tokens. We introduce an innovative approach to detect and mitigate memorization in diffusion models.
  arXiv  Detail & Related papers  (2024-03-17T01:27:00Z)
• Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits! [51.668411293817464]
  Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines. Academic research is often restrained to public datasets on the order of ten thousand samples. We devise an approach to generate a benchmark of difficulty from a pool of available samples.
  arXiv  Detail & Related papers  (2023-12-25T21:25:55Z)
• Retrieval-Enhanced Contrastive Vision-Text Models [61.783728119255365]
  We propose to equip vision-text models with the ability to refine their embedding with cross-modal retrieved information from a memory at inference time. Remarkably, we show that this can be done with a light-weight, single-layer, fusion transformer on top of a frozen CLIP. Our experiments validate that our retrieval-enhanced contrastive (RECO) training improves CLIP performance substantially on several challenging fine-grained tasks.
  arXiv  Detail & Related papers  (2023-06-12T15:52:02Z)
• Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models [14.330863905963442]
  We compare 17 modern metrics for evaluating the overall performance, fidelity, diversity, rarity, and memorization of generative models. We find that the state-of-the-art perceptual realism of diffusion models as judged by humans is not reflected in commonly reported metrics such as FID. Next, we investigate data memorization, and find that generative models do memorize training examples on simple, smaller datasets like CIFAR10, but not necessarily on more complex datasets like ImageNet.
  arXiv  Detail & Related papers  (2023-06-07T18:00:00Z)
• Mode-Aware Continual Learning for Conditional Generative Adversarial Networks [27.28511396131235]
  We introduce a new continual learning approach for conditional generative adversarial networks. First, the generator produces samples of existing modes for subsequent replay. The discriminator is then used to compute the mode similarity measure. A label for the target mode is generated and given as a weighted average of the labels within this set.
  arXiv  Detail & Related papers  (2023-05-19T03:00:31Z)
• A Memory Transformer Network for Incremental Learning [64.0410375349852]
  We study class-incremental learning, a training setup in which new classes of data are observed over time for the model to learn from. Despite the straightforward problem formulation, the naive application of classification models to class-incremental learning results in the "catastrophic forgetting" of previously seen classes. One of the most successful existing methods has been the use of a memory of exemplars, which overcomes the issue of catastrophic forgetting by saving a subset of past data into a memory bank and utilizing it to prevent forgetting when training future tasks.
  arXiv  Detail & Related papers  (2022-10-10T08:27:28Z)
• 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.