Across many industries, visual quality assurance has transitioned from a manual, labor-
intensive, and error-prone task to a fully automated and precise assessment of industrial …

With the increasing demand for data privacy, federated learning (FL) has gained popularity
for various applications. Most existing FL works focus on the classification task, overlooking …

Anomaly detection is important in many real-life applications. Recently, self-supervised
learning has greatly helped deep anomaly detection by recognizing several geometric …

Anomaly detection aims at identifying deviant samples from the normal data distribution.
Contrastive learning has provided a successful way to sample representation that enables …

This paper explores how machine learning can help classify aid activities by sector using the
OECD Creditor Reporting System (CRS). The CRS is a key source of data for monitoring …

Surgical workflow anticipation is the task of predicting the timing of relevant surgical events
from live video data, which is critical in RAS. Accurate predictions require the use of spatial …

Deep anomaly detection has recently seen significant developments to provide robust and
efficient classifiers using only a few anomalous samples. Many of those models consist in a …

Unsupervised Anomaly detection (AD) requires building a notion of normalcy, distinguishing
in-distribution (ID) and out-of-distribution (OOD) data, using only available ID samples …

Deep anomaly detection has become over the years an appealing solution in many fields,
and has seen many recent developments. One of the most promising avenues is the use of …

Web data and computational models can play important roles in analyzing cultural trends.
The current study presents an analysis of 23,492 sneaker images and metadata collected …