This article examines recent data on compression efficiency and data usage for hardware and software decoding and explores how this data shapes the value proposition for publishers opting for software ...

Abstract: In this paper, we propose a novel Average autoencoder (AE)-based amplify-and-forward ... and destination nodes are equipped with neural network (NN)-based encoder and decoder, while a ...

Researchers have developed an alternative method to encode information in synthetic molecules, which they used to encode and then decode an 11-character password to unlock a computer. The work is ...

To overcome this limitation, we present a simple but effective multimodal DL baseline by following a deep encoder–decoder network architecture, EndNet for short, for the classification of ...

there is no adversarial network, nor any image loss, robustness with regards to JPEG is done by stopping the gradient (see Towards Robust Deep Hiding Under Non-Differentiable Distortions for Practical ...

A vision encoder is a necessary component for allowing many leading LLMs to be able to work with images uploaded by users.

ACE-Step bridges this gap by integrating diffusion-based generation with Sana’s Deep Compression AutoEncoder (DCAE) and a lightweight linear transformer. It further leverages MERT and m-hubert to ...