118 citations · 288 across the 19 of their papers we have counts for
6 papers · 1 filter
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.…
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…
A Theoretical Investigation of Graph Degree as an Unsupervised Normality Measure
Caglar Aytekin, Francesco Cricri, Lixin Fan +1
For a graph representation of a dataset, a straightforward normality measure for a sample can be its graph degree. Considering a weighted graph, degree of a sample is the sum of th…
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations
Caglar Aytekin, Xingyang Ni, Francesco Cricri +1
Clustering is essential to many tasks in pattern recognition and computer vision. With the advent of deep learning, there is an increasing interest in learning deep unsupervised re…
Video Ladder Networks
Francesco Cricri, Xingyang Ni, Mikko Honkala +2
We present the Video Ladder Network (VLN) for efficiently generating future video frames. VLN is a neural encoder-decoder model augmented at all layers by both recurrent and feedfo…