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Related publications (30)

Detecting Anomalies and Obstacles in Road Scenes

Krzysztof Maciej Lis

We address the problem of segmenting anomalies and unusual obstacles in road scenes for the purpose of self-driving safety. The objects in question are not present in the common training sets as it is not feasible to collect and annotate examples for every ...

EPFL2023

Generalization of an Encoder-Decoder LSTM model for flood prediction in ungauged catchments

Olga Fink, Yi Zhang, Peter Molnar, Silvan Ragettli

Flood prediction in ungauged catchments is usually conducted by hydrological models that are parameterized based on nearby and similar gauged catchments. As an alternative to this process-based modelling, deep learning (DL) models have demonstrated their a ...

ELSEVIER2022

Leveraging Self-Supervision for Cross-Domain Crowd Counting

Pascal Fua, Nikita Durasov, Weizhe Liu

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from ...

IEEE COMPUTER SOC2022

Discriminative clustering with representation learning with any ratio of labeled to unlabeled data

We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...

2022

Contrastive Feature Learning for Railway Infrastructure Fault Diagnostic

Olga Fink

To operate the railway system safely and efficiently, a multitude of assets need to me monitored. Railway sleepers are one of these infrastructure assets, that are safety critical. To automate the monitoring process, data-driven fault diagnostics models ha ...

Research Publishing2022

Annotation-efficient image anomaly detection

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar

The present invention proposes a method for detecting anomalous or out-of-distribution images in a machine learning system (1) comprising a pre-training network with a first encoder, and an anomaly detection network with a second encoder. The system is fir ...

2022

Enforcing Connectivity of 3D Linear Structures Using Their 2D Projections

Pascal Fua, Doruk Oner, Mateusz Kozinski, Hussein Fadl Hassan Hassan Osman

Many biological and medical tasks require the delineation of 3D curvilinear structures such as blood vessels and neurites from image volumes. This is typically done using neural networks trained by minimizing voxel-wise loss functions that do not capture t ...

SPRINGER INTERNATIONAL PUBLISHING AG2022

Efficient Transformer-Based Speech Recognition

Apoorv Vyas

Training deep neural network based Automatic Speech Recognition (ASR) models often requires thousands of hours of transcribed data, limiting their use to only a few languages. Moreover, current state-of-the-art acoustic models are based on the Transformer ...

EPFL2022

Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

Boi Faltings, Aleksei Triastcyn

We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing ...

MDPI2022

How Does Pre-Trained Wav2Vec 2.0 Perform On Domain-Shifted Asr? An Extensive Benchmark On Air Traffic Control Communications

Petr Motlicek, Juan Pablo Zuluaga Gomez, Amrutha Prasad

Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine-tuned on downstream tasks e.g., automatic speech recognition (ASR). Yet, few work ...

IEEE2022

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