This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"
Fast and scalable uncertainty quantification for neural molecular property prediction, accelerated optimization, and guided virtual screening.
[ICCV 2021 Oral] Deep Evidential Action Recognition
[ICLR 2024 Spotlight] R-EDL: Relaxing Nonessential Settings of Evidential Deep Learning
[ECCV 2022] Dual-Evidential Learning for Weakly-supervised Temporal Action Localization
Implementation of "Evidential Deep Learning to Quantify Classification Uncertainty" proposing a method to quantify uncertainty in a neural network.
[TPAMI 2025] Revisiting Essential and Non-Essential Settings of Evidential Deep Learning
Official implementation of MICCAI2024 paper "Evidential Concept Embedding Models: Towards Reliable Concept Explanations for Skin Disease Diagnosis"
[ICML 2026] Revisiting Uncertainty: On Evidential Learning for Partially Relevant Video Retrieval
Repository for "Improving evidential deep learning via multi-task learning," published in AAAI2022
Calibrating LLMs with Information-Theoretic Evidential Deep Learning (ICLR 2025)
[NeurIPS 2024] Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
Hybrid Uncertainty Quantification for Bioactivity Assessment
Our Conflict-aware Evidential Deep Learning (C-EDL) method enhances robustness to OOD and adversarial inputs by combining evidence from metamorphic transformations and reducing evidence when conflicts arise, signalling higher uncertainty.
Label-free confidence estimation for neural networks - benchmarking MSP, MC Dropout, EDL, and Deep Ensembles with a novel unsupervised uncertainty metric.
Multi-label Chest X-ray classification on ChestMNIST using LISA (Language-Invariant Semantic Anchoring) and PaQ (Pathology-as-Query) with conformal safety guarantees, evidential deep learning, and post-hoc calibration.
Fourier Domain Adaptation + Evidential Deep Learning for source-free cross-domain deforestation detection in the Brazilian Amazon using Landsat-8 satellite imagery. ConvNeXt encoder (9.5M params) with per-pixel uncertainty estimation. PyTorch implementation.
Code for training Mosaico mosquito classifier: fine-tunes a 50M-parameter CNN on a 15-species ISS dataset with modified Evidential Deep Learning for open-set recognition and uncertainty estimation.
Custom Implementation of EviVLM Paper DOI: 10.1109/TMI.2025.3622492
Add a description, image, and links to the evidential-deep-learning topic page so that developers can more easily learn about it.
To associate your repository with the evidential-deep-learning topic, visit your repo's landing page and select "manage topics."