Visual Concept Connectome (VCC): Open World Concept Discovery and their Interlayer Connections in Deep Models
(2404.02233)Abstract
Understanding what deep network models capture in their learned representations is a fundamental challenge in computer vision. We present a new methodology to understanding such vision models, the Visual Concept Connectome (VCC), which discovers human interpretable concepts and their interlayer connections in a fully unsupervised manner. Our approach simultaneously reveals fine-grained concepts at a layer, connection weightings across all layers and is amendable to global analysis of network structure (e.g., branching pattern of hierarchical concept assemblies). Previous work yielded ways to extract interpretable concepts from single layers and examine their impact on classification, but did not afford multilayer concept analysis across an entire network architecture. Quantitative and qualitative empirical results show the effectiveness of VCCs in the domain of image classification. Also, we leverage VCCs for the application of failure mode debugging to reveal where mistakes arise in deep networks.
Overview
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The Visual Concept Connectome (VCC) is a new method that interprets deep neural networks by identifying human-understandable concepts and their interconnections.
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VCC segments images based on feature activations and uses clustering to uncover and quantify concepts across network layers.
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A distinct feature of this research is the measure of concept contributions between layers through the Interlayer Testing with Concept Activation Vectors (ITCAV) method.
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Empirical validation shows VCC's effectiveness in various architecture models, aiding the uncovering of model biases and improving AI interpretability.
Visual Concept Connectome: Unveiling Conceptual Relationships in Deep Neural Networks
Introduction
The Visual Concept Connectome (VCC) provides a novel methodology for interpreting deep neural network models by uncovering human-interpretable concepts and their interconnections throughout the network. This paper introduces a structured and unsupervised approach for visualizing the internal representations formed within deep learning models, specifically focusing on the domain of image classification. The primary contribution of the VCC lies in its ability to map out the hierarchical conceptual structure inherent within these models, which has remained largely opaque with prevailing interpretation techniques.
Feature Space Segmentations
The methodology begins with segmenting images based on feature activations within the network. Unlike previous techniques that rely on pixel-level or single-layer analysis, this method recursively clusters activations across selected layers, maintaining a correlation with the network's hierarchical concept assembly. Each cluster corresponds to a concept, with the degree of abstraction increasing with layer depth. This approach underpins the subsequent extraction of concepts and quantification of their interlayer relationships.
Concept Discovery
At its core, the concept discovery process involves clustering pre-segmented activations across each selected layer to yield quantifiable, interpretable concepts. By leveraging global average pooling and k-means clustering, the process effectively reduces high-dimensional activation tensors to manageable clusters representing distinct concepts. These concepts, characterized by their centroids and associated image segments, form nodes in the resulting connectome graph.
Interlayer Concept Connectivity
A pivotal aspect of this research is the quantification of concept contributions between layers, facilitated by the innovative Interlayer Testing with Concept Activation Vectors (ITCAV) method. ITCAV extends the notion of Concept Activation Vectors (CAVs) and measures the sensitivity of the activation of a deeper layer concept to changes in an earlier layer concept. This sensitivity not only demarcates the directed edges in the VCC graph but also offers a probabilistic assessment of the hierarchical nature of concept formation and abstraction within the network.
Empirical Validation
Validation of the VCC's components, including feature space segmentation, concept discovery, and interlayer connectivity, was conducted on standard image classification models exhibiting diverse architectures. Results stress the efficacy and universality of the methodology in exposing the nuanced interplay of concepts within these models. For instance, models trained on ImageNet showed a linear progression of concept abstraction, while those trained on different tasks, such as CLIP, demonstrated distinct patterns in concept assembly and influence.
Practical Applications and Future Directions
Beyond illuminating the inner workings of neural networks, VCCs show promise in identifying model failure modes and sources of bias, paving the way for more interpretable and fair AI systems. The methodology’s adaptability to different network architectures and tasks underscores its potential as a versatile tool in the AI interpretability toolkit.
Conclusion
The development of the Visual Concept Connectome marks a significant advancement in our understanding of how deep neural networks process information to make decisions. By uncovering the interlayer connections and hierarchical concept assemblies, the VCC offers a window into the previously opaque internal workings of these models. This research not only contributes to the field of explainable AI but also sets the stage for future explorations into model diagnostics, debiasing, and the development of inherently interpretable models.
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