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DG-Mix: Domain Generalization for Anomalous Sound Detection Based on Self-Supervised Learning

Related publications (32)

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Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, w ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Multikernel Regression with Sparsity Constraint

Michaël Unser, Shayan Aziznejad

In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised l ...

2021

TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?

Alexandre Massoud Alahi, Yuejiang Liu, Bastien Germain C. Van Delft, Taylor Ferdinand Mordan, Parth Ashit Kothari

Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to tackle distributional shifts. Despite encouraging results, it remains unclear when this approach thrives or fails. In this work, we first provide an in-depth look at ...

2021

Robust Learning-Augmented Caching: An Experimental Study

Adam Teodor Polak

Effective caching is crucial for performance of modern-day computing systems. A key optimization problem arising in caching – which item to evict to make room for a new item – cannot be optimally solved without knowing the future. There are many classical ...

PMLR2021

Word-level Embeddings for Cross-Task Transfer Learning in Speech Processing

Milos Cernak, Pierre Anton Beckmann

Recent breakthroughs in deep learning often rely on representation learning and knowledge transfer. In recent years, unsupervised and self-supervised techniques for learning speech representation were developed to foster automatic speech recognition. Up to ...

EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP2021

Self-supervised learning for time series classification

Anne-Sophie Brigitte Van De Velde

Time series classification (TSC) is an important and challenging problem in machine learning. In this work, we tackle the problem of TSC by first applying a Bidirectional Encoder Representations from Transformers (BERT) model, and then applying a convoluti ...

2020

In this thesis, we propose new algorithms to solve inverse problems in the context of biomedical images. Due to ill-posedness, solving these problems require some prior knowledge of the statistics of the underlying images. The traditional algorithms, in th ...

EPFL2020

Learning stereo reconstruction with deep neural networks

Stepan Tulyakov

Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these ...

EPFL2020

Roundtable Panel Discussion at DAC 2019: Evolutionary Computing or Heuristic Forever?

Giovanni De Micheli, Massimiliano Di Ventra

As demands for computing have been continuously increasing, various solutions have been proposed. Some approaches deal with improving algorithms and software programs, mainly through the tuning of advanced heuristics and learning methods. Some other tackle ...

2020

Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

Pascal Fua, Mathieu Salzmann, Carlos Joaquin Becker, Róger Bermúdez Chacón, Okan Altingövde

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences—motifs that are visually similar across different acquisitions—to infer changes on the parame ...

2019