Disentangling Regional Primitives for Image Generation
Zhengting Chen, Lei Cheng, Lianghui Ding, Liang Lin, Quanshi Zhang
TL;DR
This paper introduces a method to interpret neural network image generation by disentangling primitive regional features, enabling explanation of the process as a superposition of interpretable components.
Contribution
It proposes a novel framework for disentangling primitive features in neural networks based on new representation properties, enhancing interpretability of image generation.
Findings
Disentangled features correspond to primitive regional patterns.
The method accurately explains image generation as a superposition of features.
Experiments verify the faithfulness of the primitive regional patterns.
Abstract
This paper explains a neural network for image generation from a new perspective, i.e., explaining representation structures for image generation. We propose a set of desirable properties to define the representation structure of a neural network for image generation, including feature completeness, spatial boundedness and consistency. These properties enable us to propose a method for disentangling primitive feature components from the intermediate-layer features, where each feature component generates a primitive regional pattern covering multiple image patches. In this way, the generation of the entire image can be explained as a superposition of these feature components. We prove that these feature components, which satisfy the feature completeness property and the linear additivity property (derived from the feature completeness, spatial boundedness, and consistency properties),…
Peer Reviews
Decision·ICLR 2025 Conference Withdrawn Submission
Strengths: 1. It seems that the OR definition introduced in this paper is dual to the AND definition in previous works, which successfully extends previous framework for generative models.
Weaknesses: 1. To be honest, I cannot digest many parts of the article. This includes, but is not limited to, the tutorial of introduction, the method, and the experiments. I encourage the authors to rephrase some mathmatical descriptions with intuitive explanations. 2. The term $(-1)^{|S|-|T|}$ in many equations appear strange to me, e.g., Eq. 1 and Eq. 10. Why should we make the score for $T$ negative when the difference of the number of regions in $S$ and $T$ is odd? Please give more details
* The paper analyzes the primitive component of the internal representation of a Deep Generation Network. * The research falls in-line with the work of explaining the generation process of a distributed neural network via the idea of AND-OR graph [1] which is exciting. It contributes to the foundation of understanding the image generation process as hierarchical compositional theory. * To model the OR interaction, the paper proposed to model it via Harsanyi's interaction theory. The paper prov
* The author shows primarily the visual explanation of the generated image, however, it’s unclear how each “component” interacts with another component and how the components together compose the entire image in a hierarchy. It would be better if the authors can clearify what does mean by interaction? * The authors also claim that the primitives interactions are minimized, however, Figure 4 shows that these components are heavily overlapped. Further clarification on the experiment part would be
Fresh and Interesting Idea: The paper brings a new approach to explainable AI by breaking down image generation into basic regional patterns using OR interactions. Strong Theoretical Foundation: The use of Harsanyi interaction theory is thoughtful and well-developed. It’s clear the authors put serious effort into the math, giving the approach a solid backbone. Promising Results: While the evaluation could be better, the visual experiments do show the method can isolate specific image regions
Despite I really like the first half of the paper, I would be somehow sceptical of the overall contribution for the following reasons. 1. Limited Quantitative Evaluation: ALL evaluations are visualisations. Quantitative metrics is indeed needed, like visual quality measurement, disentanglement testing. As it stands, we’re left guessing about the true effectiveness beyond what's visually apparent. Also, the absence of comparisons with other explainable methhods weakens the case for this approach
The paper presents an interesting approach for extending Harsanyi interaction for the image generative case. It provides a theory and builds an algorithm for matching the individual regions to disentangled representations. It finds and proves an interesting OR relationship that is implemented by the model via the disentangled features. The qualitative results show a clear correspondence between image regions and extracted features. This is a very interesting complementary result to the "AND" int
1. Presentation - My main concern is the clarity of the paper, which needs significantly more work. The paper is full of details and notations and is hard to follow. The presentation of the AND interaction is not directly related to this paper (e.g., Theorem 3.1/Corollary 3.2 are not mentioned anywhere after the preliminaries) and introduces more notation that is reintroduced later anyway (e.g., defining N twice). On the other hand, some of the notations are not defined (e.g., lambda in line 294
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Taxonomy
TopicsLaw, AI, and Intellectual Property · Artificial Intelligence Applications
MethodsSparse Evolutionary Training