In this paper, we propose a novel variable-rate learned image compression framework with a conditional autoencoder. Previous learning-based image compression methods mostly require training separate ...

Accurate modeling of protein–protein complex structures is essential for understanding biological mechanisms. Hydrogen–deuterium exchange (HDX) experiments provide valuable insights into binding ...

We approach the problem of 3-D poststack seismic data compression by training a model based on a deep autoencoder. Our network architecture is trained to consider the similarity between 3-D seismic ...

It's a very impressive job! Well done. I am wondering if you have conducted any further experiments on vector quantization. The DCAE-f128 can compress a 256x256 image into a 2x2 feature map, result ...

I would like to kindly request if you could consider adding a training pipeline for the DCAE (Deep Compression Autoencoder). This addition could greatly benefit the research community and streamline ...

Deep Render, which is developing an AI-powered video compression algorithm, has raised $9 million in a fresh VC tranche.

2.1 Autoencoder The autoencoder defines a feedforward multilayer neural network with bottlenecks of conformational symmetry as shown in Figure 1. First, the data flows go through multiple successive ...