Search Results for author:
Found 9 papers, 3 papers with code
Stylist: Style-Driven Feature Ranking for Robust Novelty Detection
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.
Environment-biased Feature Ranking for Novelty Detection Robustness
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.
Self-supervised Hypergraphs for Learning Multiple World Interpretations
Thus, each node could be an input node in some hyperedges and an output node in others.
Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!
We introduce a formalization and benchmark for the unsupervised anomaly detection task in the distribution-shift scenario.
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection
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
Self-Supervised Learning in Multi-Task Graphs through Iterative Consensus Shift
The human ability to synchronize the feedback from all their senses inspired recent works in multi-task and multi-modal learning.
Iterative Knowledge Exchange Between Deep Learning and Space-Time Spectral Clustering for Unsupervised Segmentation in Videos
A novel spectral space-time clustering process on the graph produces unsupervised segmentation masks passed to the network as pseudo-labels.
Spacetime Graph Optimization for Video Object Segmentation
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.
Unsupervised object segmentation in video by efficient selection of highly probable positive features
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.
