Inducing Sparse Coding and And-Or Grammar from Generator Network
Xianglei Xing, Song-Chun Zhu, Ying Nian Wu

TL;DR
This paper presents a method to induce sparse coding and And-Or grammar in generator networks, enabling the learning of interpretable hierarchical image representations from primitive features to whole objects.
Contribution
It introduces a novel approach to enhance explainability in generative models by applying sparse operations and inducing hierarchical structures.
Findings
Learned meaningful hierarchical representations
Captured primitives, parts, and objects layer by layer
Produced explainable generative models
Abstract
We introduce an explainable generative model by applying sparse operation on the feature maps of the generator network. Meaningful hierarchical representations are obtained using the proposed generative model with sparse activations. The convolutional kernels from the bottom layer to the top layer of the generator network can learn primitives such as edges and colors, object parts, and whole objects layer by layer. From the perspective of the generator network, we propose a method for inducing both sparse coding and the AND-OR grammar for images. Experiments show that our method is capable of learning meaningful and explainable hierarchical representations.
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Taxonomy
TopicsNeural Networks and Applications · Algorithms and Data Compression · Advanced Data Compression Techniques
