IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks

📝 Original Info

• Title: IB-GAN: Disentangled Representation Learning with Information Bottleneck Generative Adversarial Networks
• ArXiv ID: 2510.20165
• Date: 2025-10-23
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (제공된 텍스트에 저자 명단이 포함되지 않음) **

📝 Abstract

We propose a new GAN-based unsupervised model for disentangled representation learning. The new model is discovered in an attempt to utilize the Information Bottleneck (IB) framework to the optimization of GAN, thereby named IB-GAN. The architecture of IB-GAN is partially similar to that of InfoGAN but has a critical difference; an intermediate layer of the generator is leveraged to constrain the mutual information between the input and the generated output. The intermediate stochastic layer can serve as a learnable latent distribution that is trained with the generator jointly in an end-to-end fashion. As a result, the generator of IB-GAN can harness the latent space in a disentangled and interpretable manner. With the experiments on dSprites and Color-dSprites dataset, we demonstrate that IB-GAN achieves competitive disentanglement scores to those of state-of-the-art \b{eta}-VAEs and outperforms InfoGAN. Moreover, the visual quality and the diversity of samples generated by IB-GAN are often better than those by \b{eta}-VAEs and Info-GAN in terms of FID score on CelebA and 3D Chairs dataset.

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