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09/19/2023
Improving CLIP Robustness with Knowledge Distillation and Self-Training
This paper examines the robustness of a multi-modal computer vision mode...
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08/17/2023
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on Regression
Uncertainty quantification is critical for deploying deep neural network...
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10/17/2022
Packed-Ensembles for Efficient Uncertainty Estimation
Deep Ensembles (DE) are a prominent approach achieving excellent perform...
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09/26/2022
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classification
Although Deep Neural Networks (DNNs) have great generalization and predi...
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07/20/2022
Latent Discriminant deterministic Uncertainty
Predictive uncertainty estimation is essential for deploying Deep Neural...
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03/02/2022
MUAD: Multiple Uncertainties for Autonomous Driving benchmark for multiple uncertainty types and tasks
Predictive uncertainty estimation is essential for deploying Deep Neural...
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02/24/2022
On Monocular Depth Estimation and Uncertainty Quantification using Classification Approaches for Regression
Monocular depth is important in many tasks, such as 3D reconstruction an...
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02/17/2022
A study of deep perceptual metrics for image quality assessment
Several metrics exist to quantify the similarity between images, but the...
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10/21/2021
SLURP: Side Learning Uncertainty for Regression Problems
It has become critical for deep learning algorithms to quantify their ou...
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08/02/2021
Reliable Semantic Segmentation with Superpixel-Mix
Along with predictive performance and runtime speed, reliability is a ke...
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07/13/2021
Learning a Discriminant Latent Space with Neural Discriminant Analysis
Discriminative features play an important role in image and object class...
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06/14/2021
Learning Deep Morphological Networks with Neural Architecture Search
Deep Neural Networks (DNNs) are generated by sequentially performing lin...
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04/24/2021
EXplainable Neural-Symbolic Learning (X-NeSyL) methodology to fuse deep learning representations with expert knowledge graphs: the MonuMAI cultural heritage use case
The latest Deep Learning (DL) models for detection and classification ha...
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12/04/2020
Encoding the latent posterior of Bayesian Neural Networks for uncertainty quantification
Bayesian neural networks (BNNs) have been long considered an ideal, yet ...
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06/01/2020
One Versus all for deep Neural Network Incertitude (OVNNI) quantification
Deep neural networks (DNNs) are powerful learning models yet their resul...
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12/24/2019
TRADI: Tracking deep neural network weight distributions
During training, the weights of a Deep Neural Network (DNN) are optimize...
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12/10/2018
Supervised Deep Kriging for Single-Image Super-Resolution
We propose a novel single-image super-resolution approach based on the g...
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05/30/2016
