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05/31/2023
RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation
Class-incremental semantic image segmentation assumes multiple model upd...
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03/20/2023
Reliability in Semantic Segmentation: Are We on the Right Track?
Motivated by the increasing popularity of transformers in computer visio...
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02/13/2023
Semantic Image Segmentation: Two Decades of Research
Semantic image segmentation (SiS) plays a fundamental role in a broad va...
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03/30/2022
On the Road to Online Adaptation for Semantic Image Segmentation
We propose a new problem formulation and a corresponding evaluation fram...
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02/02/2022
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
Recently, Wong et al. showed that adversarial training with single-step ...
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12/06/2021
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey
Semantic segmentation plays a fundamental role in a broad variety of com...
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02/24/2021
Automatic Feature Extraction for Heartbeat Anomaly Detection
We focus on automatic feature extraction for raw audio heartbeat sounds,...
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12/08/2020
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning
Most standard learning approaches lead to fragile models which are prone...
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11/29/2020
Accelerating MCMC algorithms through Bayesian Deep Networks
Markov Chain Monte Carlo (MCMC) algorithms are commonly used for their v...
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08/15/2020
Reliable Uncertainties for Bayesian Neural Networks using Alpha-divergences
Bayesian Neural Networks (BNNs) often result uncalibrated after training...
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08/15/2020
Natural Wake-Sleep Algorithm
The benefits of using the natural gradient are well known in a wide rang...
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05/14/2020
Constraining the Reionization History using Bayesian Normalizing Flows
The next generation 21 cm surveys open a new window onto the early stage...
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05/04/2020
Parameters Estimation from the 21 cm signal using Variational Inference
Upcoming experiments such as Hydrogen Epoch of Reionization Array (HERA)...
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03/13/2020
Explainable Deep Classification Models for Domain Generalization
Conventionally, AI models are thought to trade off explainability for lo...
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03/13/2020
Learning Unbiased Representations via Mutual Information Backpropagation
We are interested in learning data-driven representations that can gener...
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01/09/2020
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
We investigate and characterize the inherent resilience of conditional G...
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12/04/2019
Natural Alpha Embeddings
Learning an embedding for a large collection of items is a popular appro...
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11/19/2019
Parameters Estimation for the Cosmic Microwave Background with Bayesian Neural Networks
In this paper, we present the first study that compares different models...
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03/28/2019
Model Vulnerability to Distributional Shifts over Image Transformation Sets
We are concerned with the vulnerability of computer vision models to dis...
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07/05/2018
Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds
In this paper we propose two novel bounds for the log-likelihood based o...
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05/30/2018
Generalizing to Unseen Domains via Adversarial Data Augmentation
We are concerned with learning models that generalize well to different ...
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11/23/2017
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
Recent works showed that Generative Adversarial Networks (GANs) can be s...
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03/18/2017
Curriculum Dropout
Dropout is a very effective way of regularizing neural networks. Stochas...
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01/11/2017