Reliable uncertainty quantification (UQ) is crucial for deploying deep learning models in safety-critical domains. Existing UQ methods often either rely on multi-pass inference, which increases computational cost, or restrict expressiveness by using only final-layer embeddings. In this work, we propose a lightweight evidential meta-model that leverages multi-layer feature fusion from a pretrained backbone, capturing both low-level features and high-level semantics to better estimate uncertainty. To further enhance epistemic fidelity, we integrate maximum weight-entropy (Max-WEnt) regularization, which encourages hypothesis diversity without altering the base network or adding test-time overhead. Experiments across two benchmark settings, medical (BACH, HAM10000, BreakHIS, DIV2K) and natural (ImageNet, SVHN, Fashion-MNIST, ImageNet-C) datasets, demonstrate consistent improvements in AUROC of out-of-distribution detection compared to prior post-hoc UQ methods. Our findings show that combining multi-layer evidential modeling with Max-WEnt provides a robust, efficient, and practical framework for trustworthy AI in high-stakes applications. The meta-model adds only ~0.8M parameters and trains in under four hours on a single 48GB GPU, making it practical for real-world deployment.

Out-of-distribution (OOD) detection and uncertainty estimation (UE) are critical components for building safe machine learning systems. In this work, we propose a novel framework that combines network inversion with classifier training to simultaneously address both OOD detection and uncertainty estimation. We extend a standard n-class classifier by adding an (n+1)-th "garbage" class to capture outliers, initially populated with random Gaussian noise. After each training epoch, we use network inversion to reconstruct inputs for all classes; incoherent reconstructions are assigned to the garbage class for retraining. This iterative cycle of training, inversion, and exclusion continues until inverted samples resemble in-distribution data and uncertainty drops, indicating learned decision boundaries and cleaner class manifolds. At inference, the model detects OOD inputs by classifying them as garbage, with confidence scores estimating uncertainty. Unlike prior methods, this approach requires no external OOD data or post-hoc calibration, providing a simple, unified solution for robust classification, OOD detection, and uncertainty estimation.

Deep neural networks often require large-scale, accurately labeled datasets to perform well, but in practice the labels are frequently corrupted by noise in medical imaging, especially instance-dependent noise. In this work, we propose a novel framework to address instance-dependent label noise by integrating three key components: (i) self- supervised pretraining using SimCLR to learn robust, noise-agnostic feature representations; (ii) an iterative pseudo-label refinement strategy employing a stage-wise consensus mechanism to progressively correct mislabeled samples; and (iii) a softmax-weighted crossentropy loss that dynamically down-weighs uncertain predictions. We validate our approach on benchmark datasets such as CIFAR10 and CIFAR-100 corrupted with synthetic noise at 20 %, 30 % and 50 % levels, demonstrating significant improvements over state-of-the-art methods. We further validated our method on Chest X-rays medical imaging datasets.