Pau Rodríguez

research

∙ 07/20/2023

The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning

The mechanisms behind the success of multi-view self-supervised learning...

0 Borja Rodríguez Gálvez, et al. ∙

research

∙ 06/01/2023

FigGen: Text to Scientific Figure Generation

The generative modeling landscape has experienced tremendous growth in r...

26 Juan A. Rodriguez, et al. ∙

research

∙ 02/10/2023

Long-Context Language Decision Transformers and Exponential Tilt for Interactive Text Environments

Text-based game environments are challenging because agents must deal wi...

1 Nicolas Gontier, et al. ∙

research

∙ 12/13/2022

3rd Continual Learning Workshop Challenge on Egocentric Category and Instance Level Object Understanding

Continual Learning, also known as Lifelong or Incremental Learning, has ...

2 Lorenzo Pellegrini, et al. ∙

research

∙ 10/19/2022

OCR-VQGAN: Taming Text-within-Image Generation

Synthetic image generation has recently experienced significant improvem...

19 Juan A. Rodriguez, et al. ∙

research

∙ 10/13/2022

MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting

Large pre-trained models have proved to be remarkable zero- and (prompt-...

10 Oscar Manas, et al. ∙

research

∙ 10/04/2022

CADet: Fully Self-Supervised Anomaly Detection With Contrastive Learning

Handling out-of-distribution (OOD) samples has become a major stake in t...

23 Charles Guille-Escuret, et al. ∙

research

∙ 08/30/2022

Constraining Representations Yields Models That Know What They Don't Know

A well-known failure mode of neural networks corresponds to high confide...

8 João Monteiro, et al. ∙

research

∙ 07/10/2022

Scaling the Number of Tasks in Continual Learning

Standard gradient descent algorithms applied to sequences of tasks are k...

9 Timothée Lesort, et al. ∙

research

∙ 05/24/2022

Workflow Discovery from Dialogues in the Low Data Regime

Text-based dialogues are now widely used to solve real-world problems. I...

5 Amine El Hattami, et al. ∙

research

∙ 04/30/2022

Foundational Models for Continual Learning: An Empirical Study of Latent Replay

Rapid development of large-scale pre-training has resulted in foundation...

12 Oleksiy Ostapenko, et al. ∙

research

∙ 04/05/2022

Data Augmentation for Intent Classification with Off-the-shelf Large Language Models

Data augmentation is a widely employed technique to alleviate the proble...

13 Gaurav Sahu, et al. ∙

research

∙ 03/30/2022

Challenges in leveraging GANs for few-shot data augmentation

In this paper, we explore the use of GAN-based few-shot data augmentatio...

11 Christopher Beckham, et al. ∙

research

∙ 11/23/2021

Multi-label Iterated Learning for Image Classification with Label Ambiguity

Transfer learning from large-scale pre-trained models has become essenti...

5 Sai Rajeswar, et al. ∙

research

∙ 11/15/2021

Continual Learning via Local Module Composition

Modularity is a compelling solution to continual learning (CL), the prob...

5 Oleksiy Ostapenko, et al. ∙

research

∙ 10/27/2021

A Survey of Self-Supervised and Few-Shot Object Detection

Labeling data is often expensive and time-consuming, especially for task...

77 Gabriel Huang, et al. ∙

research

∙ 08/21/2021

SSR: Semi-supervised Soft Rasterizer for single-view 2D to 3D Reconstruction

Recent work has made significant progress in learning object meshes with...

19 Issam Laradji, et al. ∙

research

∙ 08/02/2021

Sequoia: A Software Framework to Unify Continual Learning Research

The field of Continual Learning (CL) seeks to develop algorithms that ac...

12 Fabrice Normandin, et al. ∙

research

∙ 04/14/2021

Can Active Learning Preemptively Mitigate Fairness Issues?

Dataset bias is one of the prevailing causes of unfairness in machine le...

13 Frederic Branchaud-Charron, et al. ∙

research

∙ 03/30/2021

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

Remote sensing and automatic earth monitoring are key to solve global-sc...

4 Oscar Manas, et al. ∙

research

∙ 03/18/2021

Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

Explainability for machine learning models has gained considerable atten...

26 Pau Rodríguez, et al. ∙

research

∙ 12/08/2020

Hierarchical Residual Attention Network for Single Image Super-Resolution

Convolutional neural networks are the most successful models in single i...

4 Parichehr Behjati, et al. ∙

research

∙ 11/14/2020

Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery

Cattle farming is responsible for 8.8% of greenhouse gas emissions world...

4 Issam Laradji, et al. ∙

research

∙ 11/06/2020

Affinity LCFCN: Learning to Segment Fish with Weak Supervision

Aquaculture industries rely on the availability of accurate fish body me...

16 Issam Laradji, et al. ∙

research

∙ 09/14/2020

Synbols: Probing Learning Algorithms with Synthetic Datasets

Progress in the field of machine learning has been fueled by the introdu...

58 Alexandre Lacoste, et al. ∙

research

∙ 09/14/2020

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...

75 Vincenzo Lomonaco, et al. ∙

research

∙ 08/05/2020

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

Super-resolution (SR) has achieved great success due to the development ...

0 Parichehr Behjati, et al. ∙

research

∙ 07/04/2020

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

Acquiring count annotations generally requires less human effort than po...

16 Issam Laradji, et al. ∙

research

∙ 07/03/2020

LOOC: Localize Overlapping Objects with Count Supervision

Acquiring count annotations generally requires less human effort than po...

3 Issam H. Laradji, et al. ∙

research

∙ 03/12/2020

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

Learning from non-stationary data remains a great challenge for machine ...

26 Massimo Caccia, et al. ∙

research

∙ 03/09/2020

Embedding Propagation: Smoother Manifold for Few-Shot Classification

Few-shot classification is challenging because the data distribution of ...

4 Pau Rodríguez, et al. ∙

research

∙ 07/19/2018

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

We propose a novel attention mechanism to enhance Convolutional Neural N...

2 Pau Rodríguez, et al. ∙

research

∙ 06/28/2018

Beyond One-hot Encoding: lower dimensional target embedding

Target encoding plays a central role when learning Convolutional Neural ...

0 Pau Rodríguez, et al. ∙

research

∙ 05/23/2018

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

Few-shot learning has become essential for producing models that general...

0 Boris N. Oreshkin, et al. ∙

research

∙ 02/06/2018

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...

0 Guillem Cucurull, et al. ∙

research

∙ 11/07/2016

Regularizing CNNs with Locally Constrained Decorrelations

Regularization is key for deep learning since it allows training more co...

0 Pau Rodríguez, et al. ∙

Pau Rodríguez

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