24 citations · 114 across the 43 of their papers we have counts for
10 papers · 1 filter
Fast and Memory-Efficient Wavelet Convolutions via I/O-Aware Reformulation
Amit Aflalo, Shahaf E. Finder, Roy Amoyal +2
Wavelet convolution (WTConv) has emerged as an increasingly popular drop-in replacement for standard convolutions, expanding a network's receptive field exponentially with the numb…
Towards Croppable Implicit Neural Representations
Maor Ashkenazi, Eran Treister
Implicit Neural Representations (INRs) have peaked interest in recent years due to their ability to encode natural signals using neural networks. While INRs allow for useful applic…
Wavelet Convolutions for Large Receptive Fields
Shahaf E. Finder, Roy Amoyal, Eran Treister +1
In recent years, there have been attempts to increase the kernel size of Convolutional Neural Nets (CNNs) to mimic the global receptive field of Vision Transformers' (ViTs) self-at…
Unsupervised Image Semantic Segmentation through Superpixels and Graph Neural Networks
Moshe Eliasof, Nir Ben Zikri, Eran Treister
Unsupervised image segmentation is an important task in many real-world scenarios where labelled data is of scarce availability. In this paper we propose a novel approach that harn…
Rethinking Unsupervised Neural Superpixel Segmentation
Moshe Eliasof, Nir Ben Zikri, Eran Treister
Recently, the concept of unsupervised learning for superpixel segmentation via CNNs has been studied. Essentially, such methods generate superpixels by convolutional neural network…
Wavelet Feature Maps Compression for Image-to-Image CNNs
Shahaf E. Finder, Yair Zohav, Maor Ashkenazi +1
Convolutional Neural Networks (CNNs) are known for requiring extensive computational resources, and quantization is among the best and most common methods for compressing them. Whi…