Self-Diagnosing Neural Models

Uncertainty Quantification & Unsupervised Confidence Estimation

Can a neural network know when it doesn't know? This project benchmarks four uncertainty quantification paradigms and introduces a novel unsupervised confidence metric that estimates model reliability without ever using ground-truth labels - applicable across all UQ architectures.

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Table of Contents

• Overview
• Motivation
• Novel Contribution
• Methods
• Results
• Visualizations
• Repository Structure
• Installation
• Usage
• Ablation Studies
• Citation
• License

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Overview

Standard neural networks are overconfident by design - their softmax outputs are routinely miscalibrated, assigning high confidence to incorrect predictions and low confidence to correct ones. This becomes critical in deployment: a model that cannot self-assess its own uncertainty is not safe to trust.

This project provides:

1. A rigorous comparative evaluation of four established UQ methods (MSP Baseline, MC Dropout, Evidential Deep Learning, Deep Ensembles) on CIFAR-10 (in-distribution) vs CIFAR-100 (out-of-distribution).
2. A novel unsupervised confidence metric that estimates model confidence using only the model's own internal signals - no labels, no human annotation, no held-out data required.
3. Comprehensive ablation studies validating every component of the proposed metric.

Benchmark: CIFAR-10 (ID) vs CIFAR-100 (OOD) · 100 training epochs · 10 classes

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Motivation

Uncertainty quantification in deep learning is a well-studied problem, but existing approaches share a fundamental limitation: evaluation requires labels. In real-world deployment - medical imaging, autonomous systems, financial prediction - you rarely have access to ground truth at inference time.

The core question driving this work:

Can we construct a confidence signal that correlates with true model errors, using only the model's own outputs and representations?

This project answers yes - and demonstrates it empirically across four different UQ architectures.

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Novel Contribution

Unsupervised Confidence Metric

The proposed metric combines four label-free signals into a unified confidence score:

Component	What It Captures
Prediction Consistency	Stability of predictions across augmented views of the same input
Entropy-Based Uncertainty	Information-theoretic uncertainty from the output distribution
Feature Space Dispersion	Spread of representations in the penultimate layer
Softmax Temperature Analysis	Sharpness of the output distribution under temperature scaling

Key result: The metric achieves Spearman correlations of up to ρ = 0.4221 with true model errors - without using a single label. Q4 accuracy (confidence in the top quartile of predictions) reaches 99.92% under MC Dropout.

Method	Spearman ρ ↑	Q4 Accuracy ↑
Baseline	0.4115	99.68%
MC Dropout	0.4221	99.92%
Evidential	0.3708	98.60%
Ensemble	0.4127	99.68%

The metric is architecture-agnostic - it plugs into any of the four UQ methods evaluated here without modification.

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Methods

1. Baseline - Maximum Softmax Probability (MSP)

Uses the maximum softmax output as a proxy for confidence. Fast, parameter-free, and a strong baseline despite its simplicity. Achieves competitive OOD detection (AUROC 0.8549) at near-zero inference overhead (0.25ms).

2. Monte Carlo Dropout (MC Dropout)

Treats dropout as approximate Bayesian inference. Runs N stochastic forward passes at test time, using variance across passes as the uncertainty estimate. Best calibration in the benchmark (ECE: 0.0097) - an order of magnitude better than other methods - at the cost of higher inference time (5.40ms).

3. Evidential Deep Learning (EDL)

Places a Dirichlet distribution over the softmax outputs, modeling second-order uncertainty from a single forward pass. Achieves the highest accuracy (91.66%) in the benchmark. Uncertainty is derived from the concentration parameters of the learned Dirichlet, not from stochastic sampling.

4. Deep Ensembles

Trains multiple independent models and uses disagreement across predictions as the uncertainty signal. Matches Baseline on AUROC and accuracy while providing well-calibrated ensemble uncertainty. Higher compute at training time; moderate inference overhead (0.85ms).

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Results

Classification Performance

Method	Accuracy ↑	ECE ↓	OOD AUROC ↑	Inference (ms)
Baseline (MSP)	91.14%	0.0323	0.8549	0.25
MC Dropout	90.78%	0.0097	0.8531	5.40
Evidential (EDL)	91.66%	0.0742	0.8440	0.25
Deep Ensemble	91.14%	0.0323	0.8549	0.85

Key Findings

• Best Accuracy: Evidential Deep Learning - 91.66%
• Best Calibration: MC Dropout - ECE of 0.0097 (3× lower than the next best)
• Best OOD Detection: Baseline & Ensemble - AUROC 0.8549
• Best Unsupervised Metric Correlation: MC Dropout - ρ = 0.4221
• Fastest Inference: Baseline & Evidential - 0.25ms per sample

Practical Takeaway

No single method dominates across all axes. MC Dropout is the best overall uncertainty estimator (calibration + unsupervised metric) at the cost of 20× inference overhead. Evidential Deep Learning is the strongest single-model classifier with no inference penalty. Ensembles offer a calibration-accuracy balance at moderate cost.

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Visualizations

Method Comparison - Accuracy / ECE / AUROC

Side-by-side comparison of key metrics across all four UQ methods.

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Confidence Distributions & Unsupervised Metric Behavior

Predicted confidence histograms and unsupervised confidence score distributions across ID and OOD datasets.

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OOD Detection - ROC Curves (CIFAR-10 ID vs CIFAR-100 OOD)

ROC curves for all four methods. AUROC measures separability between in-distribution and out-of-distribution samples.

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Training Curves

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Reliability Diagrams

Reliability diagrams measure calibration quality - a perfectly calibrated model lies on the diagonal. MC Dropout's diagram shows near-perfect alignment.

Left to right: Baseline · MC Dropout · Evidential · Ensemble

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Unsupervised Metric Analysis - Per Method

Unsupervised confidence score behavior per method - showing correlation with true error rates.

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Repository Structure

self-diagnosing-neural-models/
│
├── self_diagnosing_neural_models_python.ipynb   # Main pipeline notebook
├── final_report.txt                              # Full quantitative results
│
├── checkpoints/                                  # Pretrained model weights
│   ├── baseline_model.pth
│   ├── mc_dropout_model.pth
│   └── evidential_model.pth
│
├── ensemble_model/                               # Ensemble member weights
│   └── ensemble_model_*.pth
│
├── images/                                       # All figures and plots
│   ├── comparison_metrics.png
│   ├── confidence_distributions.png
│   ├── ood_roc_curves.png
│   ├── *_training_curves.png
│   ├── *_reliability_diagram.png
│   ├── *_unsupervised_analysis.png
│   └── ablation_*.png
│
└── LICENSE

Core Modules

Module	Role
DatasetManager	CIFAR-10/100 loading, augmentation pipelines
BaselineModel	MSP confidence estimation
MCDropoutModel	Stochastic inference with dropout
EvidentialModel	Dirichlet-based uncertainty output
DeepEnsemble	Multi-model ensemble training and inference
UnsupervisedConfidenceMetric	Novel label-free confidence estimation
ComprehensiveEvaluator	ECE, AUROC, Spearman ρ, Q4 accuracy
AblationStudies	Component-level ablations for the proposed metric
Visualizer	All plots and reliability diagrams

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Installation

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate          # Linux/macOS
.\.venv\Scripts\Activate.ps1       # Windows PowerShell

# Install dependencies
pip install numpy scipy scikit-learn matplotlib seaborn tqdm tensorboard

# Install PyTorch - match your CUDA version
# See https://pytorch.org/get-started/locally/ for the right wheel
pip install torch torchvision

Requirements: Python 3.9+ · PyTorch 2.0+ · CUDA optional (CPU-compatible)

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Usage

Open self_diagnosing_neural_models_python.ipynb in Jupyter or VS Code and run cells sequentially. The main_pipeline() function orchestrates the full experiment.

models, results, unsupervised_results, evaluator = main_pipeline(
    train_models=False,     # set True to retrain from scratch
    num_epochs=100,
    run_ablations=True,
    id_dataset='cifar10',
    ood_dataset='cifar100',
    batch_size=128
)

Flags:

• train_models=False - loads pretrained checkpoints from checkpoints/, skips retraining
• run_ablations=True - runs full ablation suite on the unsupervised metric
• FAST_DEBUG_SUBSET=1 (env var) or --smoke-test flag - runs on a data subset for quick validation

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Ablation Studies

Three ablation axes validate the design of the unsupervised confidence metric:

Ablation	Variable	Finding
Dropout Rate	MC Dropout inference stochasticity	Optimal range: 0.1–0.3
Ensemble Size	Number of ensemble members	Diminishing returns beyond 5
Component Weights	Relative contribution of each metric signal	Entropy + consistency dominate

Full ablation plots in images/ablation_*.png.

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Citation

If you use this work or build on the unsupervised confidence metric, please cite:

@misc{roy2025selfdiagnosing,
  title        = {Self-Diagnosing Neural Models: Uncertainty Quantification and Unsupervised Confidence Estimation},
  author       = {Sourav Roy},
  year         = {2025},
  note         = {Preprint. Available at: https://doi.org/10.13140/RG.2.2.14384.01285}
}

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License

This project is licensed under the MIT License - see LICENSE for details.

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_{Built by Sourav Roy · Founding AI/ML Engineer · Yuga AI}