Interpretable Measurement of CNN Deep Feature Density using Copula and the Generalized Characteristic Function

2024-11-07

arXiv.org

AI Summary

This study addresses the challenge of interpretable modeling of deep-layer feature distributions in convolutional neural networks (CNNs), overcoming limitations of conventional density estimation and intuitive understanding in high-dimensional spaces. We propose a novel nonparametric framework that integrates copula theory with the method of orthogonal moments (MOM) to directly estimate generalized characteristic functions (GCFs), enabling faithful modeling of non-Gaussian, statistically dependent deep features. Key findings include: (i) marginal distributions of deep nonnegative features evolve progressively from Gaussian-like (shallow layers) toward exponential distributions (deep layers); (ii) statistical independence strengthens within typical-value regions, whereas extreme large values exhibit strong copula dependence—indicating semantically meaningful, high-confidence responses rather than anomalies. Our work establishes a new theoretical foundation and quantitative toolkit for probing intrinsic mechanisms of deep models, trustworthy anomaly detection, and interpretable representation learning.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Reasoning under Uncertainty: Relational Probabilistic Models
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

Abstract

We present a novel empirical approach toward measuring the Probability Density Function (PDF) of the deep features of Convolutional Neural Networks (CNNs). Measurement of the deep feature PDF is a valuable problem for several reasons. Notably, a. Understanding the deep feature PDF yields new insight into deep representations. b. Feature density methods are important for tasks such as anomaly detection which can improve the robustness of deep learning models in the wild. Interpretable measurement of the deep feature PDF is challenging due to the Curse of Dimensionality (CoD), and the Spatial intuition Limitation. Our novel measurement technique combines copula analysis with the Method of Orthogonal Moments (MOM), in order to directly measure the Generalized Characteristic Function (GCF) of the multivariate deep feature PDF. We find that, surprisingly, the one-dimensional marginals of non-negative deep CNN features after major blocks are not well approximated by a Gaussian distribution, and that these features increasingly approximate an exponential distribution with increasing network depth. Furthermore, we observe that deep features become increasingly independent with increasing network depth within their typical ranges. However, we surprisingly also observe that many deep features exhibit strong dependence (either correlation or anti-correlation) with other extremely strong detections, even if these features are independent within typical ranges. We elaborate on these findings in our discussion, where we propose a new hypothesis that exponentially infrequent large valued features correspond to strong computer vision detections of semantic targets, which would imply that these large-valued features are not outliers but rather an important detection signal.

Problem

• Deep Learning Models
• CNN Features Distribution
• Model Reliability

Innovation

• Copula-Generalized Characteristic Function
• Deep Feature Density Interpretation
• Feature Independence and Correlation Analysis