Vis-Lens：用扩散模型生成神经网络偏好的视觉图像
Structured Visual Landscape: Generating Preferred Representations in Biological and Artificial Neural Networks
一篇研究论文：Vis-Lens 把 VAE 和扩散模型结合起来，能生成让神经元或脑区“最喜欢”看的图像，比之前的方法更真实，还不用逐图优化。
这篇论文提出结构化视觉表征景观（structured visual landscape），并设计基于激活的先验来正则化该景观。其模型 Vis-Lens 将变分自编码器与扩散模型整合进图像生成框架，直接修改激活正则化的潜码，无需逐图迭代优化即可生成自然逼真的偏好图像。作者在人工神经网络和人类视觉皮层（使用 fMRI 数据）上验证了框架效果。与近期的 state-of-the-art 方法相比，Vis-Lens 在激活调控和图像真实性两方面均有提升。
Structured Visual Landscape: Generating Preferred Representations in Biological and Artificial Neural Networks
Understanding how neurons respond to visual stimulus is an important question in both deep learning and neuroscience. It has significant implications for enhancing the interpretability of black-box artificial neural networks and for understanding the visual representation in the human brain. We propose a structured visual representations landscape and design an activation-based prior that allows us to effectively regularize the landscape with activations from brain regions or neural network units. Our model Vis-Lens integrates a variational autoencoder and a diffusion model into an image generation framework. It enables the generation of natural, realistic preferred images by directly modifying the activation-regularized latent codes without per-image iterative optimization. We demonstrate the effectiveness of our framework on both artificial neural networks and human visual cortex using functional magnetic resonance imaging (fMRI) data. Our framework improves activation modulation and image realism compared with a recent state-of-the-art method for generating preferred visual representations of these neural targets.