704 citations · 3.6k across the 98 of their papers we have counts for
122 papers
A Self Supervised StyleGAN for Image Annotation and Classification with Extremely Limited Labels
Dana Cohen Hochberg, Hayit Greenspan, Raja Giryes
The recent success of learning-based algorithms can be greatly attributed to the immense amount of annotated data used for training. Yet, many datasets lack annotations due to the…
On The Relationship Between Universal Adversarial Attacks And Sparse Representations
Dana Weitzner, Raja Giryes
The prominent success of neural networks, mainly in computer vision tasks, is increasingly shadowed by their sensitivity to small, barely perceivable adversarial perturbations in i…
An information-Theoretic Approach to Semi-supervised Transfer Learning
Daniel Jakubovitz, David Uliel, Miguel Rodrigues +1
Transfer learning is a valuable tool in deep learning as it allows propagating information from one "source dataset" to another "target dataset", especially in the case of a small…
AutoSAM: Adapting SAM to Medical Images by Overloading the Prompt Encoder
Tal Shaharabany, Aviad Dahan, Raja Giryes +1
The recently introduced Segment Anything Model (SAM) combines a clever architecture and large quantities of training data to obtain remarkable image segmentation capabilities. Howe…
Group Orthogonalization Regularization For Vision Models Adaptation and Robustness
Yoav Kurtz, Noga Bar, Raja Giryes
As neural networks become deeper, the redundancy within their parameters increases. This phenomenon has led to several methods that attempt to reduce the correlation between convol…
SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling
Alexandre Binninger, Amir Hertz, Olga Sorkine-Hornung +2
We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature. Our method allows users to quickly and easily ske…
Dense and Aligned Captions (DAC) Promote Compositional Reasoning in VL Models
Sivan Doveh, Assaf Arbelle, Sivan Harary +9
Vision and Language (VL) models offer an effective method for aligning representation spaces of images and text, leading to numerous applications such as cross-modal retrieval, vis…
Pruning at Initialization -- A Sketching Perspective
Noga Bar, Raja Giryes
The lottery ticket hypothesis (LTH) has increased attention to pruning neural networks at initialization. We study this problem in the linear setting. We show that finding a sparse…
Neural (Tangent Kernel) Collapse
Mariia Seleznova, Dana Weitzner, Raja Giryes +2
This work bridges two important concepts: the Neural Tangent Kernel (NTK), which captures the evolution of deep neural networks (DNNs) during training, and the Neural Collapse (NC)…
UDPM: Upsampling Diffusion Probabilistic Models
Shady Abu-Hussein, Raja Giryes
Denoising Diffusion Probabilistic Models (DDPM) have recently gained significant attention. DDPMs compose a Markovian process that begins in the data domain and gradually adds nois…
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
Dina Bashkirova, Samarth Mishra, Diala Lteif +16
Label-efficient and reliable semantic segmentation is essential for many real-life applications, especially for industrial settings with high visual diversity, such as waste sortin…
Set-the-Scene: Global-Local Training for Generating Controllable NeRF Scenes
Dana Cohen-Bar, Elad Richardson, Gal Metzer +2
Recent breakthroughs in text-guided image generation have led to remarkable progress in the field of 3D synthesis from text. By optimizing neural radiance fields (NeRF) directly fr…
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI
Daniel M. Lang, Eli Schwartz, Cosmin I. Bercea +2
Self-supervised models allow (pre-)training on unlabeled data and therefore have the potential to overcome the need for large annotated cohorts. One leading self-supervised model i…
TEXTure: Text-Guided Texturing of 3D Shapes
Elad Richardson, Gal Metzer, Yuval Alaluf +2
In this paper, we present TEXTure, a novel method for text-guided generation, editing, and transfer of textures for 3D shapes. Leveraging a pretrained depth-to-image diffusion mode…
ADIR: Adaptive Diffusion for Image Reconstruction
Shady Abu-Hussein, Tom Tirer, Raja Giryes
Denoising diffusion models have recently achieved remarkable success in image generation, capturing rich information about natural image statistics. This makes them highly promisin…
Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures
Gal Metzer, Elad Richardson, Or Patashnik +2
Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score dis…
MAEDAY: MAE for few and zero shot AnomalY-Detection
Eli Schwartz, Assaf Arbelle, Leonid Karlinsky +4
We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous regions are harder to…
PIP: Positional-encoding Image Prior
Nimrod Shabtay, Eli Schwartz, Raja Giryes
In Deep Image Prior (DIP), a Convolutional Neural Network (CNN) is fitted to map a latent space to a degraded (e.g. noisy) image but in the process learns to reconstruct the clean…
Teaching Structured Vision&Language Concepts to Vision&Language Models
Sivan Doveh, Assaf Arbelle, Sivan Harary +8
Vision and Language (VL) models have demonstrated remarkable zero-shot performance in a variety of tasks. However, some aspects of complex language understanding still remain a cha…
Learning Low Dimensional State Spaces with Overparameterized Recurrent Neural Nets
Edo Cohen-Karlik, Itamar Menuhin-Gruman, Raja Giryes +2
Overparameterization in deep learning typically refers to settings where a trained neural network (NN) has representational capacity to fit the training data in many ways, some of…
A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images
Dominik J. E. Waibel, Ernst Röell, Bastian Rieck +2
Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the in…
Utilizing Excess Resources in Training Neural Networks
Amit Henig, Raja Giryes
In this work, we suggest Kernel Filtering Linear Overparameterization (KFLO), where a linear cascade of filtering layers is used during training to improve network performance in t…
Membership Inference Attack Using Self Influence Functions
Gilad Cohen, Raja Giryes
Member inference (MI) attacks aim to determine if a specific data sample was used to train a machine learning model. Thus, MI is a major privacy threat to models trained on private…
ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
Shahaf Ettedgui, Shady Abu-Hussein, Raja Giryes
Using synthetic data for training neural networks that achieve good performance on real-world data is an important task as it can reduce the need for costly data annotation. Yet, s…