Jose M. Alvarez

Personalized Federated Learning with First Order Model Optimization

While federated learning traditionally aims to train a single global mod...
12/15/2020 ∙ by Michael Zhang, et al. ∙ 17 ∙ share

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Scalable Active Learning for Object Detection

Deep Neural Networks trained in a fully supervised fashion are the domin...
04/09/2020 ∙ by Elmar Haussmann, et al. ∙ 13 ∙ share

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Context Based Emotion Recognition using EMOTIC Dataset

In our everyday lives and social interactions we often try to perceive t...
03/30/2020 ∙ by Ronak Kosti, et al. ∙ 2 ∙ share

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Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

We introduce DeepInversion, a new method for synthesizing images from th...
12/18/2019 ∙ by Hongxu Yin, et al. ∙ 27 ∙ share

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Cost Volume Pyramid Based Depth Inference for Multi-View Stereo

We propose a cost volume based neural network for depth inference from m...
12/18/2019 ∙ by Jiayu Yang, et al. ∙ 18 ∙ share

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VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks

Improving weight sparsity is a common strategy for producing light-weigh...
09/10/2019 ∙ by Shuang Gao, et al. ∙ 0 ∙ share

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Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions

Semantic segmentation with Convolutional Neural Networks is a memory-int...
07/27/2019 ∙ by Kashyap Chitta, et al. ∙ 3 ∙ share

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Less is More: An Exploration of Data Redundancy with Active Dataset Subsampling

Deep Neural Networks (DNNs) often rely on very large datasets for traini...
05/29/2019 ∙ by Kashyap Chitta, et al. ∙ 0 ∙ share

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The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning

One of the main challenges of deep learning tools is their inability to ...
11/29/2018 ∙ by Jiaming Zeng, et al. ∙ 0 ∙ share

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ExpandNets: Exploiting Linear Redundancy to Train Small Networks

While very deep networks can achieve great performance, they are ill-sui...
11/26/2018 ∙ by Shuxuan Guo, et al. ∙ 0 ∙ share

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Large-Scale Visual Active Learning with Deep Probabilistic Ensembles

Annotating the right data for training deep neural networks is an import...
11/08/2018 ∙ by Kashyap Chitta, et al. ∙ 0 ∙ share

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Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization

In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scala...
11/06/2018 ∙ by Kashyap Chitta, et al. ∙ 0 ∙ share

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Effective Use of Synthetic Data for Urban Scene Semantic Segmentation

Training a deep network to perform semantic segmentation requires large ...
07/16/2018 ∙ by Fatemeh Sadat Saleh, et al. ∙ 0 ∙ share

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Compression-aware Training of Deep Networks

In recent years, great progress has been made in a variety of applicatio...
11/07/2017 ∙ by Jose M. Alvarez, et al. ∙ 0 ∙ share

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Domain-adaptive deep network compression

Deep Neural Networks trained on large datasets can be easily transferred...
09/04/2017 ∙ by Marc Masana, et al. ∙ 0 ∙ share

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Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation

Pixel-level annotations are expensive and time-consuming to obtain. Henc...
08/15/2017 ∙ by Fatemeh Sadat Saleh, et al. ∙ 0 ∙ share

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Class-Weighted Convolutional Features for Visual Instance Search

Image retrieval in realistic scenarios targets large dynamic datasets of...
07/09/2017 ∙ by Albert Jimenez, et al. ∙ 0 ∙ share

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Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation

Pixel-level annotations are expensive and time consuming to obtain. Henc...
06/06/2017 ∙ by Fatemeh Sadat Saleh, et al. ∙ 0 ∙ share

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Invertible Conditional GANs for image editing

Generative Adversarial Networks (GANs) have recently demonstrated to suc...
11/19/2016 ∙ by Guim Perarnau, et al. ∙ 0 ∙ share

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Learning the Number of Neurons in Deep Networks

Nowadays, the number of layers and of neurons in each layer of a deep ne...
11/19/2016 ∙ by Jose M. Alvarez, et al. ∙ 0 ∙ share

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Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

Pixel-level annotations are expensive and time consuming to obtain. Henc...
09/02/2016 ∙ by Fatemehsadat Saleh, et al. ∙ 0 ∙ share

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Learning Image Matching by Simply Watching Video

This work presents an unsupervised learning based approach to the ubiqui...
03/19/2016 ∙ by Gucan Long, et al. ∙ 0 ∙ share

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Road Detection by One-Class Color Classification: Dataset and Experiments

Detecting traversable road areas ahead a moving vehicle is a key process...
12/11/2014 ∙ by Jose M. Alvarez, et al. ∙ 0 ∙ share

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Road Detection via On--line Label Transfer

Vision-based road detection is an essential functionality for supporting...
12/10/2014 ∙ by Jose M. Alvarez, et al. ∙ 0 ∙ share

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Jose M. Alvarez

Featured Co-authors

Jan Kautz

Mathieu Salzmann

Xin Liu

Sanja Fidler

Hongdong Li

Joost van de Weijer

Stephen Gould

Xavier Giro-i-Nieto

Martial Hebert

Lars Petersson

Antonio M. Lopez

Personalized Federated Learning with First Order Model Optimization

Scalable Active Learning for Object Detection

Context Based Emotion Recognition using EMOTIC Dataset

Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

Cost Volume Pyramid Based Depth Inference for Multi-View Stereo

VACL: Variance-Aware Cross-Layer Regularization for Pruning Deep Residual Networks

Quadtree Generating Networks: Efficient Hierarchical Scene Parsing with Sparse Convolutions

Less is More: An Exploration of Data Redundancy with Active Dataset Subsampling

The Relevance of Bayesian Layer Positioning to Model Uncertainty in Deep Bayesian Active Learning

ExpandNets: Exploiting Linear Redundancy to Train Small Networks

Large-Scale Visual Active Learning with Deep Probabilistic Ensembles

Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization

Effective Use of Synthetic Data for Urban Scene Semantic Segmentation

Compression-aware Training of Deep Networks

Domain-adaptive deep network compression

Bringing Background into the Foreground: Making All Classes Equal in Weakly-supervised Video Semantic Segmentation

Class-Weighted Convolutional Features for Visual Instance Search

Incorporating Network Built-in Priors in Weakly-supervised Semantic Segmentation

Invertible Conditional GANs for image editing

Learning the Number of Neurons in Deep Networks

Built-in Foreground/Background Prior for Weakly-Supervised Semantic Segmentation

Learning Image Matching by Simply Watching Video

Road Detection by One-Class Color Classification: Dataset and Experiments

Road Detection via On--line Label Transfer