Deep Ensembles

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6 papers in the last four weeks, up 100% on the four weeks before. 0.1% of all new papers.

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AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification
May 15, 2026Salma Hoque Talukdar Koli, Fahima Haque Talukder JelyPlant Disease Classification Ensemble Learning
RTM Al-Kabir Technical University, Sylhet-3100, Bangladesh · North East University Bangladesh, Sylhet, Bangladesh
U-SEG: Uncertainty in SEGmentation -- A systematic multi-variable exploration
May 14, 2026Michael Smith, Frank P. FerrieImage Segmentation Panoptic Segmentation
Centre for Intelligent Machines, McGill University
bde: A Python Package for Bayesian Deep Ensembles via MILE
May 13, 2026Vyron Arvanitis, Angelos Aslanidis, Emanuel Sommer +1Deep Ensembles
Faculty of Physics, LMU Munich, Munich, Germany · Department of Statistics, LMU Munich, Munich, Germany · 3Munich Center for Machine Learning, Munich, Germany
TabSurv: Adapting Modern Tabular Neural Networks to Survival Analysis
May 5, 2026Stanislav Kirpichenko, Andrei Konstantinov, Lev UtkinSurvival Analysis Deep Ensembles
Higher School of Artificial Intelligence Technologies, Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia
Robust Deepfake Detection: Mitigating Spatial Attention Drift via Calibrated Complementary Ensembles
Apr 28, 2026Minh-Khoa Le-Phan, Minh-Hoang Le, Trong-Le Do +1Image Corruption Robustness Deep Ensembles
University of Science, VNU-HCM, Ho Chi Minh City, Vietnam · Vietnam National University, Ho Chi Minh City, Vietnam
A Tale of Two Variances: When Single-Seed Benchmarks Fail in Bayesian Deep Learning
Apr 25, 2026Qishi Zhan, Minxuan Hu, Liang He +2Bayesian Neural Networks Deep Ensembles
Department of Mathematical and Statistical Sciences Marquette University, USA · Cornell Ann S. Bowers College of Computing and Information Science Cornell University, USA · Physics Science and Engineering Tongji University, China +2
From Pixels to Explanations: Interpretable Diabetic Retinopathy Grading with CNN-Transformer Ensembles, Visual Explainability and Vision-Language Models
Apr 25, 2026Pir Bakhsh Khokhar, Carmine Gravino, Fabio Palomba +2Diabetic Retinopathy Grading Deep Ensembles
Department of Informatics, University of Salerno, Via Giovanni Paolo II, 132, Fisciano, 84084, Salerno, Italy · Department of Information Security and Communication Technology (IIK), Norwegian University of Science and Technology, Gjøvik, 2815, Norway
Towards E-Value Based Stopping Rules for Bayesian Deep Ensembles
Apr 20, 2026Emanuel Sommer, Rickmer Schulte, Sarah Deubner +2Markov Chain Monte Carlo Early Stopping
LMU Munich, MCML
Uncertainty Quantification in PINNs for Turbulent Flows: Bayesian Inference and Repulsive Ensembles
Apr 18, 2026Khemraj Shukla, Zongren Zou, Theo Kaeufer +2Uncertainty Quantification Deep Ensembles
Division of Applied Mathematics, Brown University, Providence, RI, USA · Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA · Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
Rethinking Uncertainty Quantification and Entanglement in Image Segmentation
Mar 19, 2026Jakob Lønborg Christensen, Vedrana Andersen Dahl, Morten Rieger Hannemose +2Uncertainty Quantification Deep Ensembles
Technical University of Denmark, DTU Compute · University of Lucerne, Faculty of Health Sciences and Medicine, Switzerland

May 15, 2026, cs.CVJ/K move · Enter open · S save

AgriMind: An Ensemble Deep Learning Framework for Multi-Class Plant Disease Classification

Salma Hoque Talukdar Koli, Fahima Haque Talukder Jely

RTM Al-Kabir Technical University, Sylhet-3100, Bangladesh · North East University Bangladesh, Sylhet, Bangladesh

Plant Disease Classification Ensemble Learning Deep Ensembles

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Plant disease detection is still largely manual in Bangladesh, where extension workers eyeball leaf samples across millions of smallholdings. We built AgriMind to automate this: an ensemble of ResNet50, EfficientNet-B0, and DenseNet121 trained on 20,638 PlantVillage images across 15 pepper, potato, and tomato disease classes. Transfer learning with frozen ImageNet backbones and 10 epochs of head-only training keeps the pipeline lightweight. Individual models hit 96--97% on the held-out test set, but averaging their softmax outputs pushes the ensemble to 99.23% -- a two-thirds cut in error rate. We tried biasing the average toward the best validation model; it backfired. Dropping any single model also hurt. Pepper and potato classify perfectly; tomato, with ten visually similar classes, still reaches 99.01%. On an NVIDIA T4 GPU the full ensemble runs at 53 FPS. Whether that translates to real-time mobile use depends on TensorFlow Lite optimization -- work we have not yet completed.

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