17 citations · 39 across the 5 of their papers we have counts for
7 papers
Neural Networks are Decision Trees
Caglar Aytekin
In this manuscript, we show that any neural network with any activation function can be represented as a decision tree. The representation is equivalence and not an approximation,…
A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality
Caglar Aytekin, Sakari Alenius, Dmytro Paliy +1
In this paper, we propose two contributions to neural network based denoising. First, we propose applying separate convolutional layers to each sub-band of discrete wavelet transfo…
Compressing Weight-updates for Image Artifacts Removal Neural Networks
Yat Hong Lam, Alireza Zare, Caglar Aytekin +4
In this paper, we present a novel approach for fine-tuning a decoder-side neural network in the context of image compression, such that the weight-updates are better compressible.…
A Compression Objective and a Cycle Loss for Neural Image Compression
Caglar Aytekin, Francesco Cricri, Antti Hallapuro +3
In this manuscript we propose two objective terms for neural image compression: a compression objective and a cycle loss. These terms are applied on the encoder output of an autoen…
Compressibility Loss for Neural Network Weights
Caglar Aytekin, Francesco Cricri, Emre Aksu
In this paper we apply a compressibility loss that enables learning highly compressible neural network weights. The loss was previously proposed as a measure of negated sparsity of…
Block-optimized Variable Bit Rate Neural Image Compression
Caglar Aytekin, Xingyang Ni, Francesco Cricri +3
In this work, we propose an end-to-end block-based auto-encoder system for image compression. We introduce novel contributions to neural-network based image compression, mainly in…