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Computer Science > Computer Vision and Pattern Recognition

arXiv:2304.08466 (cs)
[Submitted on 17 Apr 2023]

Title:Synthetic Data from Diffusion Models Improves ImageNet
Classification

Authors:Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad
Norouzi, David J. Fleet
Download a PDF of the paper titled Synthetic Data from Diffusion
Models Improves ImageNet Classification, by Shekoofeh Azizi and 4
other authors
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    Abstract: Deep generative models are becoming increasingly
    powerful, now generating diverse high fidelity photo-realistic
    samples given text prompts. Have they reached the point where
    models of natural images can be used for generative data
    augmentation, helping to improve challenging discriminative
    tasks? We show that large-scale text-to image diffusion models
    can be fine-tuned to produce class conditional models with SOTA
    FID (1.76 at 256x256 resolution) and Inception Score (239 at
    256x256). The model also yields a new SOTA in Classification
    Accuracy Scores (64.96 for 256x256 generative samples, improving
    to 69.24 for 1024x1024 samples). Augmenting the ImageNet training
    set with samples from the resulting models yields significant
    improvements in ImageNet classification accuracy over strong
    ResNet and Vision Transformer baselines.

          Computer Vision and Pattern Recognition (cs.CV); Artificial
Subjects: Intelligence (cs.AI); Computation and Language (cs.CL);
          Machine Learning (cs.LG)
Cite as:  arXiv:2304.08466 [cs.CV]
          (or arXiv:2304.08466v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2304.08466
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Shekoofeh Azizi [view email]
[v1] Mon, 17 Apr 2023 17:42:29 UTC (29,460 KB)
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