Adriana Romero Soriano

Michal Drozdzal

2024-03-26

ArXiv (prépublication)

DP-RDM: Adapting Diffusion Models to Private Domains Without Fine-Tuning

Jonathan Lebensold

Maziar Sanjabi

Pietro Astolfi

Kamalika Chaudhuri

Mike Rabbat

Chuan Guo

2024-03-21

ArXiv (prépublication)

GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning

Aarash Feizi

Randall Balestriero

Arantxa Casanova

Reihaneh Rabbany

We propose Guided Positive Sampling Self-Supervised Learning (GPS-SSL), a general method to inject a priori knowledge into Self-Supervised L… (voir plus)earning (SSL) positive samples selection. Current SSL methods leverage Data-Augmentations (DA) for generating positive samples and incorporate prior knowledge - an incorrect, or too weak DA will drastically reduce the quality of the learned representation. GPS-SSL proposes instead to design a metric space where Euclidean distances become a meaningful proxy for semantic relationship. In that space, it is now possible to generate positive samples from nearest neighbor sampling. Any prior knowledge can now be embedded into that metric space independently from the employed DA. From its simplicity, GPS-SSL is applicable to any SSL method, e.g. SimCLR or BYOL. A key benefit of GPS-SSL is in reducing the pressure in tailoring strong DAs. For example GPS-SSL reaches 85.58% on Cifar10 with weak DA while the baseline only reaches 37.51%. We therefore move a step forward towards the goal of making SSL less reliant on DA. We also show that even when using strong DAs, GPS-SSL outperforms the baselines on under-studied domains. We evaluate GPS-SSL along with multiple baseline SSL methods on numerous downstream datasets from different domains when the models use strong or minimal data augmentations. We hope that GPS-SSL will open new avenues in studying how to inject a priori knowledge into SSL in a principled manner.

2024-01-03

ArXiv (prépublication)

DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic Diversity

Melissa Hall

Candace Ross

Adina Williams

Nicolas Carion

Michal Drozdzal

The unprecedented photorealistic results achieved by recent text-to-image generative systems and their increasing use as plug-and-play conte… (voir plus)nt creation solutions make it crucial to understand their potential biases. In this work, we introduce three indicators to evaluate the realism, diversity and prompt-generation consistency of text-to-image generative systems when prompted to generate objects from across the world. Our indicators complement qualitative analysis of the broader impact of such systems by enabling automatic and efficient benchmarking of geographic disparities, an important step towards building responsible visual content creation systems. We use our proposed indicators to analyze potential geographic biases in state-of-the-art visual content creation systems and find that: (1) models have less realism and diversity of generations when prompting for Africa and West Asia than Europe, (2) prompting with geographic information comes at a cost to prompt-consistency and diversity of generated images, and (3) models exhibit more region-level disparities for some objects than others. Perhaps most interestingly, our indicators suggest that progress in image generation quality has come at the cost of real-world geographic representation. Our comprehensive evaluation constitutes a crucial step towards ensuring a positive experience of visual content creation for everyone. Code is available at https://github.com/facebookresearch/DIG-In/.

2024-01-02

TMLR (accepté)

A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions

Jack Urbanek

Florian Bordes

Pietro Astolfi

Mary Williamson

Vasu Sharma

2023-12-14

ArXiv (prépublication)

Feedback-guided Data Synthesis for Imbalanced Classification

Reyhane Askari Hemmat

Mohammad Pezeshki

Florian Bordes

Michal Drozdzal

Current status quo in machine learning is to use static datasets of real images for training, which often come from long-tailed distribution… (voir plus)s. With the recent advances in generative models, researchers have started augmenting these static datasets with synthetic data, reporting moderate performance improvements on classification tasks. We hypothesize that these performance gains are limited by the lack of feedback from the classifier to the generative model, which would promote the usefulness of the generated samples to improve the classifier's performance. In this work, we introduce a framework for augmenting static datasets with useful synthetic samples, which leverages one-shot feedback from the classifier to drive the sampling of the generative model. In order for the framework to be effective, we find that the samples must be close to the support of the real data of the task at hand, and be sufficiently diverse. We validate three feedback criteria on a long-tailed dataset (ImageNet-LT) as well as a group-imbalanced dataset (NICO++). On ImageNet-LT, we achieve state-of-the-art results, with over 4 percent improvement on underrepresented classes while being twice efficient in terms of the number of generated synthetic samples. NICO++ also enjoys marked boosts of over 5 percent in worst group accuracy. With these results, our framework paves the path towards effectively leveraging state-of-the-art text-to-image models as data sources that can be queried to improve downstream applications.

2023-10-30

NeurIPS.cc/2023/Workshop/SyntheticData4ML (poster)

Improved baselines for vision-language pre-training

Enrico Fini

Pietro Astolfi

Jakob Verbeek

Michal Drozdzal

2023-10-05

TMLR (accepté)

Controllable Image Generation via Collage Representations

Arantxa Casanova

Marlene Careil

Chris Pal

Jakob Verbeek

Michal Drozdzal

2023-04-26

ArXiv (prépublication)

Instance-Conditioned GAN Data Augmentation for Representation Learning

Pietro Astolfi

Arantxa Casanova

Jakob Verbeek

Pascal Vincent

Michal Drozdzal

2023-03-16

ArXiv (prépublication)

Learning to Substitute Ingredients in Recipes

Bahare Fatemi

Quentin Duval

Rohit Girdhar

Michal Drozdzal

Recipe personalization through ingredient substitution has the potential to help people meet their dietary needs and preferences, avoid pote… (voir plus)ntial allergens, and ease culinary exploration in everyone's kitchen. To address ingredient substitution, we build a benchmark, composed of a dataset of substitution pairs with standardized splits, evaluation metrics, and baselines. We further introduce Graph-based Ingredient Substitution Module (GISMo), a novel model that leverages the context of a recipe as well as generic ingredient relational information encoded within a graph to rank plausible substitutions. We show through comprehensive experimental validation that GISMo surpasses the best performing baseline by a large margin in terms of mean reciprocal rank. Finally, we highlight the benefits of GISMo by integrating it in an improved image-to-recipe generation pipeline, enabling recipe personalization through user intervention. Quantitative and qualitative results show the efficacy of our proposed system, paving the road towards truly personalized cooking and tasting experiences.

2023-02-15

ArXiv (prépublication)

On the Challenges of using Reinforcement Learning in Precision Drug Dosing: Delay and Prolongedness of Action Effects

Sumana Basu

M. Legault

Doina Precup

Drug dosing is an important application of AI, which can be formulated as a Reinforcement Learning (RL) problem. In this paper, we identify … (voir plus)two major challenges of using RL for drug dosing: delayed and prolonged effects of administering medications, which break the Markov assumption of the RL framework. We focus on prolongedness and define PAE-POMDP (Prolonged Action Effect-Partially Observable Markov Decision Process), a subclass of POMDPs in which the Markov assumption does not hold specifically due to prolonged effects of actions. Motivated by the pharmacology literature, we propose a simple and effective approach to converting drug dosing PAE-POMDPs into MDPs, enabling the use of the existing RL algorithms to solve such problems. We validate the proposed approach on a toy task, and a challenging glucose control task, for which we devise a clinically-inspired reward function. Our results demonstrate that: (1) the proposed method to restore the Markov assumption leads to significant improvements over a vanilla baseline; (2) the approach is competitive with recurrent policies which may inherently capture the prolonged affect of actions; (3) it is remarkably more time and memory efficient than the recurrent baseline and hence more suitable for real-time dosing control systems; and (4) it exhibits favourable qualitative behavior in our policy analysis.

2023-01-02

ArXiv (prépublication)

Graph Inductive Biases in Transformers without Message Passing

Liheng Ma

Chen Lin

Derek Lim

Puneet K. Dokania

Mark Coates

Philip Torr

Ser-Nam Lim

Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial fo… (voir plus)r Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit known issues of message-passing, and differ significantly from Transformers used in other domains, thus making transfer of research advances more difficult. On the other hand, Graph Transformers without message-passing often perform poorly on smaller datasets, where inductive biases are more crucial. To bridge this gap, we propose the Graph Inductive bias Transformer (GRIT) -- a new Graph Transformer that incorporates graph inductive biases without using message passing. GRIT is based on several architectural changes that are each theoretically and empirically justified, including: learned relative positional encodings initialized with random walk probabilities, a flexible attention mechanism that updates node and node-pair representations, and injection of degree information in each layer. We prove that GRIT is expressive -- it can express shortest path distances and various graph propagation matrices. GRIT achieves state-of-the-art empirical performance across a variety of graph datasets, thus showing the power that Graph Transformers without message-passing can deliver.

2023-01-01

ICML (publié)