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05/08/2023
Domain Agnostic Image-to-image Translation using Low-Resolution Conditioning
Generally, image-to-image translation (i2i) methods aim at learning mapp...
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03/04/2023
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes
Neural network ensembles have been studied extensively in the context of...
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10/19/2022
On Learning Fairness and Accuracy on Multiple Subgroups
We propose an analysis in fair learning that preserves the utility of th...
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05/26/2022
Fair Representation Learning through Implicit Path Alignment
We consider a fair representation learning perspective, where optimal pr...
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04/02/2022
Matching Feature Sets for Few-Shot Image Classification
In image classification, it is common practice to train deep networks to...
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07/23/2021
Image-to-Image Translation with Low Resolution Conditioning
Most image-to-image translation methods focus on learning mappings acros...
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05/30/2021
On the benefits of representation regularization in invariance based domain generalization
A crucial aspect in reliable machine learning is to design a deployable ...
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05/09/2021
Aggregating From Multiple Target-Shifted Sources
Multi-source domain adaptation aims at leveraging the knowledge from mul...
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03/05/2021
Meta Learning Black-Box Population-Based Optimizers
The no free lunch theorem states that no model is better suited to every...
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02/12/2021
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images
Traditionally, the main focus of image super-resolution techniques is on...
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11/24/2020
Persistent Mixture Model Networks for Few-Shot Image Classification
We introduce Persistent Mixture Model (PMM) networks for representation ...
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07/30/2020
Beyond ℋ-Divergence: Domain Adaptation Theory With Jensen-Shannon Divergence
We reveal the incoherence between the widely-adopted empirical domain ad...
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05/23/2020
Self-supervised Robust Object Detectors from Partially Labelled datasets
In the object detection task, merging various datasets from similar cont...
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05/17/2020
Toward Adversarial Robustness by Diversity in an Ensemble of Specialized Deep Neural Networks
We aim at demonstrating the influence of diversity in the ensemble of CN...
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02/07/2020
Input Dropout for Spatially Aligned Modalities
Computer vision datasets containing multiple modalities such as color, d...
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12/11/2019
Associative Alignment for Few-shot Image Classification
Few-shot image classification aims at training a model by using only a f...
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11/25/2019
A Novel Unsupervised Post-Processing Calibration Method for DNNS with Robustness to Domain Shift
The uncertainty estimation is critical in real-world decision making app...
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11/20/2019
Deep Active Learning: Unified and Principled Method for Query and Training
In this paper, we proposed a unified and principled method for both quer...
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10/19/2019
Deep Parametric Indoor Lighting Estimation
We present a method to estimate lighting from a single image of an indoo...
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10/18/2019
Toward Metrics for Differentiating Out-of-Distribution Sets
Vanilla CNNs, as uncalibrated classifiers, suffer from classifying out-o...
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05/01/2019
Unsupervised Temperature Scaling: Post-Processing Unsupervised Calibration of Deep Models Decisions
Great performances of deep learning are undeniable, with impressive resu...
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03/21/2019
A Principled Approach for Learning Task Similarity in Multitask Learning
Multitask learning aims at solving a set of related tasks simultaneously...
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03/04/2019
Learning of Image Dehazing Models for Segmentation Tasks
To evaluate their performance, existing dehazing approaches generally re...
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10/27/2018
A New Loss Function for Temperature Scaling to have Better Calibrated Deep Networks
However Deep neural networks recently have achieved impressive results f...
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10/26/2018
Accumulating Knowledge for Lifelong Online Learning
Lifelong learning can be viewed as a continuous transfer learning proced...
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08/21/2018
Controlling Over-generalization and its Effect on Adversarial Examples Generation and Detection
Convolutional Neural Networks (CNNs) allowed improving the state-of-the-...
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06/18/2018
Evaluating and Characterizing Incremental Learning from Non-Stationary Data
Incremental learning from non-stationary data poses special challenges t...
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04/24/2018
Towards Dependable Deep Convolutional Neural Networks (CNNs) with Out-distribution Learning
Detection and rejection of adversarial examples in security sensitive an...
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03/28/2018
Learning to Become an Expert: Deep Networks Applied To Super-Resolution Microscopy
With super-resolution optical microscopy, it is now possible to observe ...
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02/22/2018
Diversity regularization in deep ensembles
Calibrating the confidence of supervised learning models is important fo...
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02/20/2018
Out-distribution training confers robustness to deep neural networks
The easiness at which adversarial instances can be generated in deep neu...
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09/13/2017
Query Completion Using Bandits for Engines Aggregation
Assisting users by suggesting completed queries as they type is a common...
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04/01/2017
Learning to Predict Indoor Illumination from a Single Image
We propose an automatic method to infer high dynamic range illumination ...
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02/22/2017
Robustness to Adversarial Examples through an Ensemble of Specialists
We are proposing to use an ensemble of diverse specialists, where specia...
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01/04/2017
Estimating Quality in Multi-Objective Bandits Optimization
Many real-world applications are characterized by a number of conflictin...
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11/05/2016
Alternating Direction Method of Multipliers for Sparse Convolutional Neural Networks
The storage and computation requirements of Convolutional Neural Network...
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04/10/2013
