Towards Difficulty-Aware Analysis of Deep Neural Networks

2025-07-01

AI Summary

Conventional model analysis predominantly focuses on erroneous predictions while overlooking inherent instance difficulty variations. Method: We propose a difficulty-aware model analysis framework that, for the first time, establishes a multidimensional instance difficulty metric grounded in data-, model-, and human-centric perspectives, and introduces a consistency evaluation mechanism between model confidence and human-perceived difficulty. Our approach integrates difficulty scoring, confidence analysis, annotation consistency checking, and interactive visualization, implemented in the DifficultyEyes tool for pattern-based diagnostic analysis. Contribution/Results: Experiments demonstrate that the framework effectively identifies canonical defects—including data noise, class imbalance, and model fragility—significantly enhancing difficult-instance detection and root-cause attribution. Empirical validation on benchmarks such as ImageNet confirms its practical utility for robustness assessment and data quality diagnosis.

Technology Category

• Machine Learning: Evaluation and Analysis
• Computer Vision: Adversarial Attacks & Robustness
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Traditional instance-based model analysis focuses mainly on misclassified instances. However, this approach overlooks the varying difficulty associated with different instances. Ideally, a robust model should recognize and reflect the challenges presented by intrinsically difficult instances. It is also valuable to investigate whether the difficulty perceived by the model aligns with that perceived by humans. To address this, we propose incorporating instance difficulty into the deep neural network evaluation process, specifically for supervised classification tasks on image data. Specifically, we consider difficulty measures from three perspectives -- data, model, and human -- to facilitate comprehensive evaluation and comparison. Additionally, we develop an interactive visual tool, DifficultyEyes, to support the identification of instances of interest based on various difficulty patterns and to aid in analyzing potential data or model issues. Case studies demonstrate the effectiveness of our approach.

Problem

• Incorporating instance difficulty into deep neural network evaluation
• Aligning model-perceived difficulty with human-perceived difficulty
• Developing visual tool for analyzing difficulty patterns and issues

Innovation

• Incorporates instance difficulty into DNN evaluation
• Measures difficulty from data, model, human perspectives
• Develops visual tool DifficultyEyes for analysis