704 citations · 3.6k across the 98 of their papers we have counts for
15 papers · 1 filter
Supervised and Unsupervised Learning of Parameterized Color Enhancement
Yoav Chai, Raja Giryes, Lior Wolf
We treat the problem of color enhancement as an image translation task, which we tackle using both supervised and unsupervised learning. Unlike traditional image to image generator…
DEGAS: Differentiable Efficient Generator Search
Sivan Doveh, Raja Giryes
Network architecture search (NAS) achieves state-of-the-art results in various tasks such as classification and semantic segmentation. Recently, a reinforcement learning-based appr…
MetAdapt: Meta-Learned Task-Adaptive Architecture for Few-Shot Classification
Sivan Doveh, Eli Schwartz, Chao Xue +4
Few-Shot Learning (FSL) is a topic of rapidly growing interest. Typically, in FSL a model is trained on a dataset consisting of many small tasks (meta-tasks) and learns to adapt to…
Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers
Shady Abu Hussein, Tom Tirer, Raja Giryes
The single image super-resolution task is one of the most examined inverse problems in the past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior perform…
MonSter: Awakening the Mono in Stereo
Yotam Gil, Shay Elmalem, Harel Haim +2
Passive depth estimation is among the most long-studied fields in computer vision. The most common methods for passive depth estimation are either a stereo or a monocular system. U…
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
Gilad Cohen, Guillermo Sapiro, Raja Giryes
Deep neural networks (DNNs) are notorious for their vulnerability to adversarial attacks, which are small perturbations added to their input images to mislead their prediction. Det…
Deep Radar Detector
Daniel Brodeski, Igal Bilik, Raja Giryes
While camera and LiDAR processing have been revolutionized since the introduction of deep learning, radar processing still relies on classical tools. In this paper, we introduce a…
Back-Projection based Fidelity Term for Ill-Posed Linear Inverse Problems
Tom Tirer, Raja Giryes
Ill-posed linear inverse problems appear in many image processing applications, such as deblurring, super-resolution and compressed sensing. Many restoration strategies involve min…
Image-Adaptive GAN based Reconstruction
Shady Abu Hussein, Tom Tirer, Raja Giryes
In the recent years, there has been a significant improvement in the quality of samples produced by (deep) generative models such as variational auto-encoders and generative advers…
Baby steps towards few-shot learning with multiple semantics
Eli Schwartz, Leonid Karlinsky, Rogerio Feris +2
Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems. While considerabl…
Blind Visual Motif Removal from a Single Image
Amir Hertz, Sharon Fogel, Rana Hanocka +2
Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image. For example, deco…
ASAP: Architecture Search, Anneal and Prune
Asaf Noy, Niv Nayman, Tal Ridnik +5
Automatic methods for Neural Architecture Search (NAS) have been shown to produce state-of-the-art network models. Yet, their main drawback is the computational complexity of the s…
Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data
Roee Levy Leshem, Raja Giryes
This paper introduces Taco-VC, a novel architecture for voice conversion based on Tacotron synthesizer, which is a sequence-to-sequence with attention model. The training of multi-…
Lautum Regularization for Semi-supervised Transfer Learning
Daniel Jakubovitz, Miguel R. D. Rodrigues, Raja Giryes
Transfer learning is a very important tool in deep learning as it allows propagating information from one "source dataset" to another "target dataset", especially in the case of a…
LaSO: Label-Set Operations networks for multi-label few-shot learning
Amit Alfassy, Leonid Karlinsky, Amit Aides +5
Example synthesis is one of the leading methods to tackle the problem of few-shot learning, where only a small number of samples per class are available. However, current synthesis…