1 code implementation • 5 Oct 2023 • Stefan Smeu, Elena Burceanu, Emanuela Haller, Andrei Liviu Nicolicioiu
Similar to works in out-of-distribution generalization, we propose to use the formalization of separating into semantic or content changes, that are relevant to our task, and style changes, that are irrelevant.
no code implementations • 21 Sep 2023 • Stefan Smeu, Elena Burceanu, Emanuela Haller, Andrei Liviu Nicolicioiu
Thus, we propose a method that starts with a pretrained embedding and a multi-env setup and manages to rank the features based on their environment-focus.
no code implementations • 15 Aug 2023 • Alina Marcu, Mihai Pirvu, Dragos Costea, Emanuela Haller, Emil Slusanschi, Ahmed Nabil Belbachir, Rahul Sukthankar, Marius Leordeanu
Thus, each node could be an input node in some hyperedges and an output node in others.
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 #5 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.