Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification

• URL: http://arxiv.org/abs/2505.22926v1
• Date: Wed, 28 May 2025 22:58:50 GMT
• Title: Leveraging Diffusion Models for Synthetic Data Augmentation in Protein Subcellular Localization Classification
• Authors: Sylvey Lin, Zhi-Yi Cao,
• Abstract summary: We implement a class-conditional denoising diffusion probabilistic model (DDPM) to produce label-consistent samples.<n>We explore their integration with real data via two hybrid training strategies: Mix Loss and Mix Representation.<n>Our findings highlight the importance of realistic data generation and robust supervision when incorporating generative augmentation into biomedical image classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We investigate whether synthetic images generated by diffusion models can enhance multi-label classification of protein subcellular localization. Specifically, we implement a simplified class-conditional denoising diffusion probabilistic model (DDPM) to produce label-consistent samples and explore their integration with real data via two hybrid training strategies: Mix Loss and Mix Representation. While these approaches yield promising validation performance, our proposed MixModel exhibits poor generalization to unseen test data, underscoring the challenges of leveraging synthetic data effectively. In contrast, baseline classifiers built on ResNet backbones with conventional loss functions demonstrate greater stability and test-time performance. Our findings highlight the importance of realistic data generation and robust supervision when incorporating generative augmentation into biomedical image classification.

Related papers

• TarDiff: Target-Oriented Diffusion Guidance for Synthetic Electronic Health Record Time Series Generation [26.116599951658454]
  Time-series generation is crucial for advancing clinical machine learning models.<n>We argue that fidelity to observed data alone does not guarantee better model performance.<n>We propose TarDiff, a novel target-oriented diffusion framework that integrates task-specific influence guidance.
  arXiv  Detail & Related papers  (2025-04-24T14:36:10Z)
• Conditional Data Synthesis Augmentation [4.3108820946281945]
  Conditional Data Synthesis Augmentation (CoDSA) is a novel framework that synthesizes high-fidelity data for improving model performance across multimodal domains.<n>CoDSA fine-tunes pre-trained generative models to enhance the realism of synthetic data and increase sample density in sparse areas.<n>We introduce a theoretical framework that quantifies the statistical accuracy improvements enabled by CoDSA as a function of synthetic sample volume and targeted region allocation.
  arXiv  Detail & Related papers  (2025-04-10T03:38:11Z)
• Balanced Mixed-Type Tabular Data Synthesis with Diffusion Models [14.651592234678722]
  Current diffusion models tend to inherit bias in the training dataset and generate biased synthetic data.<n>We introduce a novel model that incorporates sensitive guidance to generate fair synthetic data with balanced joint distributions of the target label and sensitive attributes.<n>Our method effectively mitigates bias in training data while maintaining the quality of the generated samples.
  arXiv  Detail & Related papers  (2024-04-12T06:08:43Z)
• DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception [78.26734070960886]
  Current perceptive models heavily depend on resource-intensive datasets. We introduce perception-aware loss (P.A. loss) through segmentation, improving both quality and controllability. Our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation.
  arXiv  Detail & Related papers  (2024-03-20T04:58:03Z)
• Training Class-Imbalanced Diffusion Model Via Overlap Optimization [55.96820607533968]
  Diffusion models trained on real-world datasets often yield inferior fidelity for tail classes. Deep generative models, including diffusion models, are biased towards classes with abundant training images. We propose a method based on contrastive learning to minimize the overlap between distributions of synthetic images for different classes.
  arXiv  Detail & Related papers  (2024-02-16T16:47:21Z)
• Combining propensity score methods with variational autoencoders for generating synthetic data in presence of latent sub-groups [0.0]
  Heterogeneity might be known, e.g., as indicated by sub-groups labels, or might be unknown and reflected only in properties of distributions, such as bimodality or skewness. We investigate how such heterogeneity can be preserved and controlled when obtaining synthetic data from variational autoencoders (VAEs), i.e., a generative deep learning technique.
  arXiv  Detail & Related papers  (2023-12-12T22:49:24Z)
• Diffusing Gaussian Mixtures for Generating Categorical Data [21.43283907118157]
  We propose a generative model for categorical data based on diffusion models with a focus on high-quality sample generation. Our method of evaluation highlights the capabilities and limitations of different generative models for generating categorical data.
  arXiv  Detail & Related papers  (2023-03-08T14:55:32Z)
• Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting [62.23057729112182]
  Differentiable score-based causal discovery methods learn a directed acyclic graph from observational data. We propose a model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore.
  arXiv  Detail & Related papers  (2023-03-06T14:49:59Z)
• Improving Adversarial Robustness by Contrastive Guided Diffusion Process [19.972628281993487]
  We propose Contrastive-Guided Diffusion Process (Contrastive-DP) to guide the diffusion model in data generation. We show that enhancing the distinguishability among the generated data is critical for improving adversarial robustness.
  arXiv  Detail & Related papers  (2022-10-18T07:20:53Z)
• CAFE: Learning to Condense Dataset by Aligning Features [72.99394941348757]
  We propose a novel scheme to Condense dataset by Aligning FEatures (CAFE) At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art.
  arXiv  Detail & Related papers  (2022-03-03T05:58:49Z)
• Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data [125.7135706352493]
  Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images. Recent studies have shown that training GANs with limited data remains formidable due to discriminator overfitting. This paper introduces a novel strategy called Adaptive Pseudo Augmentation (APA) to encourage healthy competition between the generator and the discriminator.
  arXiv  Detail & Related papers  (2021-11-12T18:13:45Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)

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.