2 citations · 2 across the 1 of their papers we have counts for
3 papers
cs.CV2021
InAugment: Improving Classifiers via Internal Augmentation
Moab Arar, Ariel Shamir, Amit Bermano
Image augmentation techniques apply transformation functions such as rotation, shearing, or color distortion on an input image. These augmentations were proven useful in improving…
cs.LG2020★ 2 cited
Focus-and-Expand: Training Guidance Through Gradual Manipulation of Input Features
Moab Arar, Noa Fish, Dani Daniel +3
We present a simple and intuitive Focus-and-eXpand (\fax) method to guide the training process of a neural network towards a specific solution. Optimizing a neural network is a hig…
cs.CV2020
Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation
Moab Arar, Yiftach Ginger, Dov Danon +3
Many applications, such as autonomous driving, heavily rely on multi-modal data where spatial alignment between the modalities is required. Most multi-modal registration methods st…