用因子分解法建立更好的图像概率模型

B. J. Culpepper, Jascha Narain Sohl-Dickstein, B. Olshausen
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引用次数: 14

摘要

我们描述了一个有向双线性模型,它学习自然图像特征之间的高阶分组。该模型用两组潜在变量表示图像:一组变量表示哪些特征组是活跃的,另一组变量表示组内的相对活跃度。这种因式表示是有益的,因为它在响应特征位置的小变化时是稳定的,同时仍然保留有关相对空间关系的信息。当在MNIST数字上训练时,结果表示使用简单的分类器提供了最先进的分类性能。当对自然图像进行训练时,该模型学习根据位置、方向和规模的接近度对特征进行分组。该模型实现了高对数似然(- 94 nats),超过了目前使用mcRBM模型可实现的自然图像的技术水平。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Building a better probabilistic model of images by factorization
We describe a directed bilinear model that learns higher-order groupings among features of natural images. The model represents images in terms of two sets of latent variables: one set of variables represents which feature groups are active, while the other specifies the relative activity within groups. Such a factorized representation is beneficial because it is stable in response to small variations in the placement of features while still preserving information about relative spatial relationships. When trained on MNIST digits, the resulting representation provides state of the art performance in classification using a simple classifier. When trained on natural images, the model learns to group features according to proximity in position, orientation, and scale. The model achieves high log-likelihood (−94 nats), surpassing the current state of the art for natural images achievable with an mcRBM model.
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