Pau Rodríguez

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Element AI Inc ∙ Universitat Autònoma de Barcelona

Synbols: Probing Learning Algorithms with Synthetic Datasets

Progress in the field of machine learning has been fueled by the introdu...
09/14/2020 ∙ by Alexandre Lacoste, et al. ∙ 58 ∙ share

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CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions

In the last few years, we have witnessed a renewed and fast-growing inte...
09/14/2020 ∙ by Vincenzo Lomonaco, et al. ∙ 64 ∙ share

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OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network

Super-resolution (SR) has achieved great success due to the development ...
08/05/2020 ∙ by Parichehr Behjati, et al. ∙ 0 ∙ share

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A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images

Acquiring count annotations generally requires less human effort than po...
07/04/2020 ∙ by Issam Laradji, et al. ∙ 16 ∙ share

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LOOC: Localize Overlapping Objects with Count Supervision

Acquiring count annotations generally requires less human effort than po...
07/03/2020 ∙ by Issam H. Laradji, et al. ∙ 3 ∙ share

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Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning

Learning from non-stationary data remains a great challenge for machine ...
03/12/2020 ∙ by Massimo Caccia, et al. ∙ 26 ∙ share

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Embedding Propagation: Smoother Manifold for Few-Shot Classification

Few-shot classification is challenging because the data distribution of ...
03/09/2020 ∙ by Pau Rodríguez, et al. ∙ 4 ∙ share

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Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery

We propose a novel attention mechanism to enhance Convolutional Neural N...
07/19/2018 ∙ by Pau Rodríguez, et al. ∙ 2 ∙ share

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Beyond One-hot Encoding: lower dimensional target embedding

Target encoding plays a central role when learning Convolutional Neural ...
06/28/2018 ∙ by Pau Rodríguez, et al. ∙ 0 ∙ share

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TADAM: Task dependent adaptive metric for improved few-shot learning

Few-shot learning has become essential for producing models that general...
05/23/2018 ∙ by Boris N. Oreshkin, et al. ∙ 0 ∙ share

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Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks

Social media, as a major platform for communication and information exch...
02/06/2018 ∙ by Guillem Cucurull, et al. ∙ 0 ∙ share

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Regularizing CNNs with Locally Constrained Decorrelations

Regularization is key for deep learning since it allows training more co...
11/07/2016 ∙ by Pau Rodríguez, et al. ∙ 0 ∙ share

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Pau Rodríguez

Featured Co-authors

Yu Chen

Sergio Escalera

David Vazquez

Laurent Charlin

Irina Rish

Alexandre Lacoste

German I. Parisi

Min Lin

Vincenzo Lomonaco

Davide Maltoni

Issam Laradji

Synbols: Probing Learning Algorithms with Synthetic Datasets

CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions

OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network

A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images

LOOC: Localize Overlapping Objects with Count Supervision

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning

Embedding Propagation: Smoother Manifold for Few-Shot Classification

Attend and Rectify: a Gated Attention Mechanism for Fine-Grained Recovery

Beyond One-hot Encoding: lower dimensional target embedding

TADAM: Task dependent adaptive metric for improved few-shot learning

Deep Inference of Personality Traits by Integrating Image and Word Use in Social Networks

Regularizing CNNs with Locally Constrained Decorrelations