no code implementations • 6 Oct 2022 • Stefan Smeu, Elena Burceanu, Andrei Liviu Nicolicioiu, Emanuela Haller
We introduce a formalization and benchmark for the unsupervised anomaly detection task in the distribution-shift scenario.
1 code implementation • 30 Jun 2022 • Marius Dragoi, Elena Burceanu, Emanuela Haller, Andrei Manolache, Florin Brad
Analyzing the distribution shift of data is a growing research direction in nowadays Machine Learning (ML), leading to emerging new benchmarks that focus on providing a suitable scenario for studying the generalization properties of ML models.
Ranked #1 on Unsupervised Anomaly Detection on AnoShift
1 code implementation • 26 Mar 2021 • Emanuela Haller, Elena Burceanu, Marius Leordeanu
The human ability to synchronize the feedback from all their senses inspired recent works in multi-task and multi-modal learning.
no code implementations • 13 Dec 2020 • Emanuela Haller, Adina Magda Florea, Marius Leordeanu
A novel spectral space-time clustering process on the graph produces unsupervised segmentation masks passed to the network as pseudo-labels.
no code implementations • 7 Jul 2019 • Emanuela Haller, Adina Magda Florea, Marius Leordeanu
While the actual matrix is not computed explicitly, the proposed algorithm efficiently computes, in a few iteration steps, the principal eigenvector that captures the segmentation of the main object in the video.
no code implementations • ICCV 2017 • Emanuela Haller, Marius Leordeanu
We also present theoretical properties of our unsupervised learning method, that under some mild constraints is guaranteed to learn a correct discriminative classifier even in the unsupervised case.