Abstract
This paper reveals that every image can be understood as a first-order norm+linear autoregressive process, referred to as FINOLA, where norm+linear denotes the use of normalization before the linear model. We demonstrate that images of size 256$\times$256 can be reconstructed from a compressed vector using autoregression up to a 16$\times$16 feature map, followed by upsampling and convolution. This discovery sheds light on the underlying partial differential equations (PDEs) governing the latent feature space. Additionally, we investigate the application of FINOLA for self-supervised learning through a simple masked prediction technique. By encoding a single unmasked quadrant block, we can autoregressively predict the surrounding masked region. Remarkably, this pre-trained representation proves effective for image classification and object detection tasks, even in lightweight networks, without requiring fine-tuning. The code will be made publicly available.
Abstract (translated)
这篇文章表明，每个图像都可以被视为一个第一阶 norms+线性 autoregressive 过程，也称为 FINOLA，其中 norms+线性表示在线性模型之前使用标准化。我们证明了，大小为 256x256 的图像可以通过自回归从压缩向量重构到 16x16 特征图，然后进行增广和卷积。这个发现揭示了支配潜在特征空间的基 partial differential equations (PDEs)。此外，我们通过简单的蒙面预测技术研究了 FINOLA 对自监督学习的应用。通过编码一个未暴露的 Quadrant 块，我们可以自回归预测周围的蒙面区域。令人惊讶地，这个预训练表示证明对于图像分类和物体检测任务有效，即使在轻量级网络中，也不需要微调。代码将公开可用。
URL
https://arxiv.org/abs/2305.16319
