21 citations · 56 across the 25 of their papers we have counts for
6 papers · 1 filter
Detecting Deepfakes Without Seeing Any
Tal Reiss, Bar Cavia, Yedid Hoshen
Deepfake attacks, malicious manipulation of media containing people, are a serious concern for society. Conventional deepfake detection methods train supervised classifiers to dist…
Set Features for Anomaly Detection
Niv Cohen, Issar Tzachor, Yedid Hoshen
This paper proposes to use set features for detecting anomalies in samples that consist of unusual combinations of normal elements. Many leading methods discover anomalies by detec…
Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge
Yedid Hoshen
In this perspective paper, we argue that the dominant paradigm in anomaly detection cannot scale indefinitely and will eventually hit fundamental limits. This is due to the a no fr…
No Free Lunch: The Hazards of Over-Expressive Representations in Anomaly Detection
Tal Reiss, Niv Cohen, Yedid Hoshen
Anomaly detection methods, powered by deep learning, have recently been making significant progress, mostly due to improved representations. It is tempting to hypothesize that anom…
Unsupervised Word Segmentation Using Temporal Gradient Pseudo-Labels
Tzeviya Sylvia Fuchs, Yedid Hoshen
Unsupervised word segmentation in audio utterances is challenging as, in speech, there is typically no gap between words. In a preliminary experiment, we show that recent deep self…
Set Features for Fine-grained Anomaly Detection
Niv Cohen, Issar Tzachor, Yedid Hoshen
Fine-grained anomaly detection has recently been dominated by segmentation based approaches. These approaches first classify each element of the sample (e.g., image patch) as norma…