Publications associées à Bilinear Multimodal Discriminator for Adversarial Domain Adaptation with Privileged Information

Publications associées (13)

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

EPFL2025

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

2025

Nonlinear processing with linear optics

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim

Deep neural networks have achieved remarkable breakthroughs by leveraging multiple layers of data processing to extract hidden representations, albeit at the cost of large electronic computing power. To enhance energy efficiency and speed, the optical impl ...

Nature Publishing Group2024

Template Inversion Attack against Face Recognition Systems using 3D Face Reconstruction

Hatef Otroshi Shahreza

Face recognition systems are increasingly being used in different applications. In such systems, some features (also known as embeddings or templates) are extracted from each face image. Then, the extracted templates are stored in the system's database dur ...

Ieee Computer Soc2023

Multi-layer state evolution under random convolutional design

Lenka Zdeborová, Florent Gérard Krzakala

Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generati ...

2023

Multi-task Learning for Joint Re-identification, Team Affiliation, and Role Classification for Sports Visual Tracking

Vladimir Dominic K. Somers

Effective tracking and re-identification of players is essential for analyzing soccer videos. But, it is a challenging task due to the non-linear motion of players, the similarity in appearance of players from the same team, and frequent occlusions. Theref ...

Assoc Computing Machinery2023

Towards Verifiable, Generalizable and Efficient Robust Deep Neural Networks

Chen Liu

In the last decade, deep neural networks have achieved tremendous success in many fields of machine learning. However, they are shown vulnerable against adversarial attacks: well-designed, yet imperceptible, perturbations can make the state-of-the-art deep ...

EPFL2022

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models

Beril Besbinar

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

EPFL2022

Uncertainty Reduction for Model Adaptation in Semantic Segmentation

François Fleuret, Prabhu Teja Sivaprasad

Traditional methods for Unsupervised Domain Adaptation (UDA) targeting semantic segmentation exploit information common to the source and target domains, using both labeled source data and unlabeled target data. In this paper, we investigate a setting wher ...

IEEE2021

Deep Generative Models and Applications

Tatjana Chavdarova

Over the past few years, there have been fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. The amount of annotated data drastically increased and supervised deep discriminative models exceed ...

EPFL2020

Improving speech embedding using crossmodal transfer learning with audio-visual data

Jean-Marc Odobez

Learning a discriminative voice embedding allows speaker turns to be compared directly and efficiently, which is crucial for tasks such as diarization and verification. This paper investigates several transfer learning approaches to improve a voice embeddi ...

2019

Machine learning methods to assist energy system optimization

Jean-Louis Scartezzini, Amarasinghage Tharindu Dasun Perera, Vahid Moussavi Nik

This study evaluates the potential of supervised and transfer learning techniques to assist energy system optimization. A surrogate model is developed with the support of a supervised learning technique (by using artificial neural network) in order to bypa ...

2019

Computational Modeling of Face-to-Face Social Interaction Using Nonverbal Behavioral Cues

Dinesh Babu Jayagopi

The computational modeling of face-to-face interactions using nonverbal behavioral cues is an emerging and relevant problem in social computing. Studying face-to-face interactions in small groups helps in understanding the basic processes of individual and ...

EPFL2011