Hugo Larochelle

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Hattie Zhou

Doctorat - UdeM

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Publications

Language GANs Falling Short

Massimo Caccia

Lucas Caccia

William Fedus

Joelle Pineau

Laurent Charlin

Generating high-quality text with sufficient diversity is essential for a wide range of Natural Language Generation (NLG) tasks. Maximum-Lik… (voir plus)elihood (MLE) models trained with teacher forcing have consistently been reported as weak baselines, where poor performance is attributed to exposure bias (Bengio et al., 2015; Ranzato et al., 2015); at inference time, the model is fed its own prediction instead of a ground-truth token, which can lead to accumulating errors and poor samples. This line of reasoning has led to an outbreak of adversarial based approaches for NLG, on the account that GANs do not suffer from exposure bias. In this work, we make several surprising observations which contradict common beliefs. First, we revisit the canonical evaluation framework for NLG, and point out fundamental flaws with quality-only evaluation: we show that one can outperform such metrics using a simple, well-known temperature parameter to artificially reduce the entropy of the model's conditional distributions. Second, we leverage the control over the quality / diversity trade-off given by this parameter to evaluate models over the whole quality-diversity spectrum and find MLE models constantly outperform the proposed GAN variants over the whole quality-diversity space. Our results have several implications: 1) The impact of exposure bias on sample quality is less severe than previously thought, 2) temperature tuning provides a better quality / diversity trade-off than adversarial training while being easier to train, easier to cross-validate, and less computationally expensive. Code to reproduce the experiments is available at github.com/pclucas14/GansFallingShort

2020-01-01

ICLR (publié)

Learning Graph Structure With A Finite-State Automaton Layer

Daniel D. Johnson

Daniel Tarlow

Small-GAN: Speeding Up GAN Training Using Core-sets

Samarth Sinha

Han Zhang

Anirudh Goyal

Augustus Odena

Recent work by Brock et al. (2018) suggests that Generative Adversarial Networks (GANs) benefit disproportionately from large mini-batch siz… (voir plus)es. Unfortunately, using large batches is slow and expensive on conventional hardware. Thus, it would be nice if we could generate batches that were effectively large though actually small. In this work, we propose a method to do this, inspired by the use of Coreset-selection in active learning. When training a GAN, we draw a large batch of samples from the prior and then compress that batch using Coreset-selection. To create effectively large batches of 'real' images, we create a cached dataset of Inception activations of each training image, randomly project them down to a smaller dimension, and then use Coreset-selection on those projected activations at training time. We conduct experiments showing that this technique substantially reduces training time and memory usage for modern GAN variants, that it reduces the fraction of dropped modes in a synthetic dataset, and that it allows GANs to reach a new state of the art in anomaly detection.

2020-01-01

ICML (publié)

proceedings.mlr.press

Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling

Tong Che

Ruixiang ZHANG

Jascha Sohl-Dickstein

Liam Paull

Yuan Cao

We show that the sum of the implicit generator log-density …

InfoBot: Structured Exploration in ReinforcementLearning Using Information Bottleneck

Anirudh Goyal

Riashat Islam

D. Strouse

Zafarali Ahmed

Matthew Botvinick

Sergey Levine

InfoBot: Transfer and Exploration via the Information Bottleneck

Anirudh Goyal

Riashat Islam

DJ Strouse

Zafarali Ahmed

Matthew Botvinick

Sergey Levine

A central challenge in reinforcement learning is discovering effective policies for tasks where rewards are sparsely distributed. We postula… (voir plus)te that in the absence of useful reward signals, an effective exploration strategy should seek out {\it decision states}. These states lie at critical junctions in the state space from where the agent can transition to new, potentially unexplored regions. We propose to learn about decision states from prior experience. By training a goal-conditioned policy with an information bottleneck, we can identify decision states by examining where the model actually leverages the goal state. We find that this simple mechanism effectively identifies decision states, even in partially observed settings. In effect, the model learns the sensory cues that correlate with potential subgoals. In new environments, this model can then identify novel subgoals for further exploration, guiding the agent through a sequence of potential decision states and through new regions of the state space.

2019-01-01

ICLR.cc/2019/Conference (poster)

Recall Traces: Backtracking Models for Efficient Reinforcement Learning

Anirudh Goyal

Philemon Brakel

William Fedus

Soumye Singhal

Timothy P. Lillicrap

Sergey Levine

In many environments only a tiny subset of all states yield high reward. In these cases, few of the interactions with the environment provid… (voir plus)e a relevant learning signal. Hence, we may want to preferentially train on those high-reward states and the probable trajectories leading to them. To this end, we advocate for the use of a backtracking model that predicts the preceding states that terminate at a given high-reward state. We can train a model which, starting from a high value state (or one that is estimated to have high value), predicts and sample for which the (state, action)-tuples may have led to that high value state. These traces of (state, action) pairs, which we refer to as Recall Traces, sampled from this backtracking model starting from a high value state, are informative as they terminate in good states, and hence we can use these traces to improve a policy. We provide a variational interpretation for this idea and a practical algorithm in which the backtracking model samples from an approximate posterior distribution over trajectories which lead to large rewards. Our method improves the sample efficiency of both on- and off-policy RL algorithms across several environments and tasks.

2019-01-01

ICLR.cc/2019/Conference (poster)

Blindfold Baselines for Embodied QA

Ankesh Anand

Eugene Belilovsky

Kyle Kastner

We explore blindfold (question-only) baselines for Embodied Question Answering. The EmbodiedQA task requires an agent to answer a question b… (voir plus)y intelligently navigating in a simulated environment, gathering necessary visual information only through first-person vision before finally answering. Consequently, a blindfold baseline which ignores the environment and visual information is a degenerate solution, yet we show through our experiments on the EQAv1 dataset that a simple question-only baseline achieves state-of-the-art results on the EmbodiedQA task in all cases except when the agent is spawned extremely close to the object.

2018-11-12

ArXiv (prépublication)

HoME: a Household Multimodal Environment

Simon Brodeur

Ethan Perez

Ankesh Anand

Florian Golemo

Luca Celotti

Florian Strub

Jean Rouat

We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction… (voir plus) with objects and other agents, all within a realistic context. HoME integrates over 45,000 diverse 3D house layouts based on the SUNCG dataset, a scale which may facilitate learning, generalization, and transfer. HoME is an open-source, OpenAI Gym-compatible platform extensible to tasks in reinforcement learning, language grounding, sound-based navigation, robotics, multi-agent learning, and more. We hope HoME better enables artificial agents to learn as humans do: in an interactive, multimodal, and richly contextualized setting.

2018-01-01

ICLR (Workshop) (publié)

GuessWhat?! Visual Object Discovery through Multi-modal Dialogue

Harm de Vries

Florian Strub

Sarath Chandar

Olivier Pietquin

We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The… (voir plus) goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks.

2017-07-21

2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (publié)

doi.org

Modulating early visual processing by language

Harm de Vries

Florian Strub

Jérémie Mary

Olivier Pietquin

It is commonly assumed that language refers to high-level visual concepts while leaving low-level visual processing unaffected. This view do… (voir plus)minates the current literature in computational models for language-vision tasks, where visual and linguistic input are mostly processed independently before being fused into a single representation. In this paper, we deviate from this classic pipeline and propose to modulate the \emph{entire visual processing} by linguistic input. Specifically, we condition the batch normalization parameters of a pretrained residual network (ResNet) on a language embedding. This approach, which we call MOdulated RESnet (\MRN), significantly improves strong baselines on two visual question answering tasks. Our ablation study shows that modulating from the early stages of the visual processing is beneficial.

2017-07-02

ArXiv (prépublication)

Movie Description

Anna Rohrbach

Atousa Torabi

Marcus Rohrbach

Niket Tandon

Chris Pal

Bernt Schiele

2017-01-25

International Journal of Computer Vision (publié)

doi.org