Self-Taught Semi-Supervised Anomaly Detection On Upper Limb X-Rays

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Seeing and recognizing an object whose size is much smaller than the illumination wavelength is a challenging task for an observer placed in the far field, due to the diffraction limit. Recent advances in near- and far-field microscopy have offered several ...

We analyze numerically the training dynamics of deep neural networks (DNN) by using methods developed in statistical physics of glassy systems. The two main issues we address are (1) the complexity of the loss landscape and of the dynamics within it, and ( ...

Modern 3D human pose estimation techniques rely on deep networks, which require large amounts of training data. While weakly-supervised methods require less supervision, by utilizing 2D poses or multi-view imagery without annotations, they still need a suf ...

The two classic image restoration tasks, demosaicing and super-resolution, have traditionally always been studied indepen- dently. That is sub-optimal as sequential processing, demosaic- ing and then super-resolution, may lead to amplification of ar- tifac ...

The continuous increase, witnessed in the last decade, of both the amount of available data and the areas of application of machine learning, has lead to a demand for both learning and planning algorithms that are capable of handling large-scale problems. ...

Self-Taught Semi-Supervised Anomaly Detection On Upper Limb X-Rays

Chat with Graph Search

Far-Field Subwavelength Acoustic Imaging by Deep Learning

Editorial: Computational Pathology

Comparing dynamics: deep neural networks versus glassy systems

Unsupervised Geometry-Aware Representation Learning for 3D Human Pose Estimation

Deep Residual Network for Joint Demosaicing and Super- Resolution

Tractable Approaches to Learning and Planning in High Dimensions