no code implementations • 3 Jan 2024 • Idit Diamant, Amir Rosenfeld, Idan Achituve, Jacob Goldberger, Arnon Netzer
In this paper, we introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings and learn to de-confuse the pseudo-labels.
no code implementations • ICLR 2020 • Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, Nir Shavit
In this work, we present a functional form which approximates well the generalization error in practice.
no code implementations • 26 Mar 2019 • Amir Rosenfeld, Richard Zemel, John K. Tsotsos
Predicting human perceptual similarity is a challenging subject of ongoing research.
1 code implementation • 9 Aug 2018 • Amir Rosenfeld, Richard Zemel, John K. Tsotsos
We showcase a family of common failures of state-of-the art object detectors.
no code implementations • 5 Mar 2018 • Amir Rosenfeld, Markus D. Solbach, John K. Tsotsos
Perceptual judgment of image similarity by humans relies on rich internal representations ranging from low-level features to high-level concepts, scene properties and even cultural associations.
no code implementations • 16 Feb 2018 • Amir Rosenfeld, John K. Tsotsos
While great advances are made in pattern recognition and machine learning, the successes of such fields remain restricted to narrow applications and seem to break down when training data is scarce, a shift in domain occurs, or when intelligent reasoning is required for rapid adaptation to new environments.
no code implementations • 13 Feb 2018 • Amir Rosenfeld, John K. Tsotsos
There is no denying the tremendous leap in the performance of machine learning methods in the past half-decade.
no code implementations • 2 Feb 2018 • Amir Rosenfeld, John K. Tsotsos
The implications of this intriguing property of deep neural networks are discussed and we suggest ways to harness it to create more robust representations.
1 code implementation • 16 Nov 2017 • Amir Rosenfeld, Mahdi Biparva, John K. Tsotsos
This process has been shown to be an effect of top-down signaling in the visual system triggered by the said cue.
no code implementations • ICLR 2018 • Amir Rosenfeld, John K. Tsotsos
Given an existing trained neural network, it is often desirable to learn new capabilities without hindering performance of those already learned.
no code implementations • 25 May 2016 • Amir Rosenfeld, Shimon Ullman
Classes in natural images tend to follow long tail distributions.
no code implementations • 14 Mar 2016 • Amir Rosenfeld, Shimon Ullman
Convolutional neural networks have been shown to develop internal representations, which correspond closely to semantically meaningful objects and parts, although trained solely on class labels.
no code implementations • 17 Jan 2016 • Amir Rosenfeld, Shimon Ullman
Action recognition in still images has seen major improvement in recent years due to advances in human pose estimation, object recognition and stronger feature representations.
no code implementations • 12 Nov 2015 • Amir Rosenfeld, Shimon Ullman
In this paper we demonstrate how recognition is improved by obtaining precise localization of the action-object and consequently extracting details of the object shape together with the actor-object interaction.