Vincent Dumoulin | DeepAI

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∙ 02/13/2023

In Search for a Generalizable Method for Source Free Domain Adaptation

Source-free domain adaptation (SFDA) is compelling because it allows ada...

0 Malik Boudiaf, et al. ∙

∙ 04/01/2022

Proper Reuse of Image Classification Features Improves Object Detection

A common practice in transfer learning is to initialize the downstream m...

0 Cristina Vasconcelos, et al. ∙

∙ 01/10/2022

Head2Toe: Utilizing Intermediate Representations for Better Transfer Learning

Transfer-learning methods aim to improve performance in a data-scarce ta...

0 Utku Evci, et al. ∙

∙ 08/07/2021

Impact of Aliasing on Generalization in Deep Convolutional Networks

We investigate the impact of aliasing on generalization in Deep Convolut...

19 Cristina Vasconcelos, et al. ∙

∙ 06/25/2021

Domain Conditional Predictors for Domain Adaptation

Learning guarantees often rely on assumptions of i.i.d. data, which will...

0 João Monteiro, et al. ∙

∙ 05/14/2021

Learning a Universal Template for Few-shot Dataset Generalization

Few-shot dataset generalization is a challenging variant of the well-stu...

15 Eleni Triantafillou, et al. ∙

∙ 04/06/2021

Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark

Meta and transfer learning are two successful families of approaches to ...

8 Vincent Dumoulin, et al. ∙

∙ 03/07/2019

Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Few-shot classification refers to learning a classifier for new classes ...

43 Eleni Triantafillou, et al. ∙

∙ 02/01/2019

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for...

54 Nolan Bard, et al. ∙

∙ 12/01/2018

Cross-Modulation Networks for Few-Shot Learning

A family of recent successful approaches to few-shot learning relies on ...

0 Hugo Prol, et al. ∙

∙ 10/19/2017

Generative Adversarial Networks: An Overview

Generative adversarial networks (GANs) provide a way to learn deep repre...

0 Antonia Creswell, et al. ∙

∙ 09/22/2017

FiLM: Visual Reasoning with a General Conditioning Layer

We introduce a general-purpose conditioning method for neural networks c...

0 Ethan Perez, et al. ∙

∙ 07/10/2017

Learning Visual Reasoning Without Strong Priors

Achieving artificial visual reasoning - the ability to answer image-rela...

0 Ethan Perez, et al. ∙

∙ 05/18/2017

Exploring the structure of a real-time, arbitrary neural artistic stylization network

In this paper, we present a method which combines the flexibility of the...

0 Golnaz Ghiasi, et al. ∙

∙ 03/31/2017

Improved Training of Wasserstein GANs

Generative Adversarial Networks (GANs) are powerful generative models, b...

0 Ishaan Gulrajani, et al. ∙

∙ 10/24/2016

A Learned Representation For Artistic Style

The diversity of painting styles represents a rich visual vocabulary for...

0 Vincent Dumoulin, et al. ∙

∙ 06/02/2016

Adversarially Learned Inference

We introduce the adversarially learned inference (ALI) model, which join...

0 Vincent Dumoulin, et al. ∙

∙ 05/09/2016

Theano: A Python framework for fast computation of mathematical expressions

Theano is a Python library that allows to define, optimize, and evaluate...

0 The Theano Development Team, et al. ∙

∙ 03/23/2016

A guide to convolution arithmetic for deep learning

We introduce a guide to help deep learning practitioners understand and ...

0 Vincent Dumoulin, et al. ∙

∙ 02/09/2016

Discriminative Regularization for Generative Models

We explore the question of whether the representations learned by classi...

0 Alex Lamb, et al. ∙

∙ 06/01/2015

Blocks and Fuel: Frameworks for deep learning

We introduce two Python frameworks to train neural networks on large dat...

0 Bart van Merriënboer, et al. ∙

∙ 12/18/2013

On the Challenges of Physical Implementations of RBMs

Restricted Boltzmann machines (RBMs) are powerful machine learning model...

0 Vincent Dumoulin, et al. ∙

∙ 08/20/2013

Pylearn2: a machine learning research library

Pylearn2 is a machine learning research library. This does not just mean...

0 Ian J. Goodfellow, et al. ∙