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DG-Mix: Domain Generalization for Anomalous Sound Detection Based on Self-Supervised Learning
Related publications (32)
Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation
Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence.Althou ...
EPFL2024Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange
Sabine Süsstrunk, Mathieu Salzmann, Tong Zhang, Yi Wu
In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...
2024Robust machine learning for neuroscientific inference
Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...
EPFL2024Topics in statistical physics of high-dimensional machine learning
In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...
EPFL2024Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning
The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...
EPFL2024Can Self-Supervised Neural Networks Pre-Trained on Human Speech distinguish Animal Callers?
Mathew Magimai Doss, Eklavya Sarkar
Self-supervised learning (SSL) models use only the intrinsic structure of a given signal, independent of its acoustic domain, to extract essential information from the input to an embedding space. This implies that the utility of such representations is no ...
ISCA2023Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer
Over the years, clinical institutes accumulated large amounts of digital slides from resected tissue specimens. These digital images, called whole slide images (WSIs), are high-resolution tissue snapshots that depict the complex interaction of cells at the ...
EPFL2023Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction
Martin Weigert, Benjamin Tobias Gallusser, Max Stieber
State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...
Cham2023Population pharmacokinetic model selection assisted by machine learning
Jan Sickmann Hesthaven, Nadia Terranova
A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...
SPRINGER/PLENUM PUBLISHERS2021Self-supervised Pre-training Enhances Change Detection in Sentinel-2 Imagery
Devis Tuia, Diego Marcos Gonzalez
While annotated images for change detection using satellite imagery are scarce and costly to obtain, there is a wealth of unlabeled images being generated every day. In order to leverage these data to learn an image representation more adequate for change ...
Springer, Cham2021