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SODA: Bottleneck Diffusion Models for Representation Learning

2023-11-29 18:53:34
Drew A. Hudson, Daniel Zoran, Mateusz Malinowski, Andrew K. Lampinen, Andrew Jaegle, James L. McClelland, Loic Matthey, Felix Hill, Alexander Lerchner

Abstract

We introduce SODA, a self-supervised diffusion model, designed for representation learning. The model incorporates an image encoder, which distills a source view into a compact representation, that, in turn, guides the generation of related novel views. We show that by imposing a tight bottleneck between the encoder and a denoising decoder, and leveraging novel view synthesis as a self-supervised objective, we can turn diffusion models into strong representation learners, capable of capturing visual semantics in an unsupervised manner. To the best of our knowledge, SODA is the first diffusion model to succeed at ImageNet linear-probe classification, and, at the same time, it accomplishes reconstruction, editing and synthesis tasks across a wide range of datasets. Further investigation reveals the disentangled nature of its emergent latent space, that serves as an effective interface to control and manipulate the model's produced images. All in all, we aim to shed light on the exciting and promising potential of diffusion models, not only for image generation, but also for learning rich and robust representations.

Abstract (translated)

我们介绍了SODA,一种自监督扩散模型,专为表示学习而设计。该模型包括一个图像编码器,将原始图像压缩成一个紧凑的表示,进而指导相关的新颖视图的生成。我们证明了通过在编码器和去噪解码器之间施加严格的瓶颈,并利用新颖视图合成作为自监督目标,我们可以将扩散模型变成强大的表示学习器,能够以无监督方式捕捉视觉语义。据我们所知,SODA是第一个在ImageNet线性探针分类中成功的扩散模型,同时,它还完成了在各种数据集上的重建、编辑和合成任务。进一步的调查揭示了其浮现式潜在空间的不分离性质,这作为控制和操作模型产生的图像的有效接口。总的来说,我们希望阐明扩散模型的令人兴奋和有前景的潜力,不仅限于图像生成,而且也适用于学习丰富的和鲁棒的表示。

URL

https://arxiv.org/abs/2311.17901

PDF

https://arxiv.org/pdf/2311.17901.pdf


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