BRICS：图像集合的双层特征表示
BRICS: Bi-level feature Representation of Image CollectionS
AI总结：
提出BRICS双层特征表示，通过自编码器将图像编码为连续关键码以检索多分辨率特征网格，结合扩散模型实现高效图像合成，在重构质量和生成性能上超越现有方法。
摘要：
我们提出了BRICS，一种用于图像集合的双层特征表示，它由位于特征网格空间之上的关键码空间组成。具体而言，我们的表示通过自编码器学习，将图像编码为连续关键码，这些关键码用于从多组多分辨率特征网格中检索特征。我们的关键码和特征网格通过明确定义的梯度流进行连续联合训练，与离散向量量化（VQ）相比，这带来了特征网格的高使用率和改进的生成建模。与现有的连续表示（如KL正则化潜码）不同，我们的关键码在尺度和方差上受到严格限制。总体而言，BRICS的特征编码紧凑、训练高效，并支持使用扩散模型对关键码进行生成建模。实验结果表明，我们的方法取得了与VQ相当的重构结果，同时具有更小、更高效的解码器网络（GFlops减少50%）。通过在我们的关键码空间上应用扩散模型，我们在FFHQ和LSUN-Church数据集的图像合成上取得了最先进的性能（CLIP-FID比LDM低29%，比StyleGAN2低32%，比Projected GAN低44%）。
英文摘要：
We present BRICS, a bi-level feature representation for image collections, which consists of a key code space on top of a feature grid space. Specifically, our representation is learned by an autoencoder to encode images into continuous key codes, which are used to retrieve features from groups of multi-resolution feature grids. Our key codes and feature grids are jointly trained continuously with well-defined gradient flows, leading to high usage rates of the feature grids and improved generative modeling compared to discrete Vector Quantization (VQ). Differently from existing continuous representations such as KL-regularized latent codes, our key codes are strictly bounded in scale and variance. Overall, feature encoding by BRICS is compact, efficient to train, and enables generative modeling over key codes using the diffusion model. Experimental results show that our method achieves comparable reconstruction results to VQ while having a smaller and more efficient decoder network (50% fewer GFlops). By applying the diffusion model over our key code space, we achieve state-of-the-art performance on image synthesis on the FFHQ and LSUN-Church (29% lower than LDM, 32% lower than StyleGAN2, 44% lower than Projected GAN on CLIP-FID) datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
