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Found 20 papers, 14 papers with code
Lightweight Diffusion Models for Resource-Constrained Semantic Communication
Despite their impressive ability to regenerate content from the compressed semantic information received, generative models pose crucial challenges for communication systems in terms of high memory footprints and heavy computational load.
Language-Oriented Semantic Latent Representation for Image Transmission
In the new paradigm of semantic communication (SC), the focus is on delivering meanings behind bits by extracting semantic information from raw data.
Rethinking Multi-User Semantic Communications with Deep Generative Models
In recent years, novel communication strategies have emerged to face the challenges that the increased number of connected devices and the higher quality of transmitted information are posing.
Demystifying the Hypercomplex: Inductive Biases in Hypercomplex Deep Learning
Hypercomplex algebras have recently been gaining prominence in the field of deep learning owing to the advantages of their division algebras over real vector spaces and their superior results when dealing with multidimensional signals in real-world 3D and 4D paradigms.
Towards Explaining Hypercomplex Neural Networks
To achieve this, we define a type of cosine-similarity transform within the parameterized hypercomplex domain.
Generative AI Meets Semantic Communication: Evolution and Revolution of Communication Tasks
Disclosing generative models capabilities in semantic communication paves the way for a paradigm shift with respect to conventional communication systems, which has great potential to reduce the amount of data traffic and offers a revolutionary versatility to novel tasks and applications that were not even conceivable a few years ago.
Generalizing Medical Image Representations via Quaternion Wavelet Networks
The proposed quaternion wavelet network (QUAVE) can be easily integrated with any pre-existing medical image analysis or synthesis task, and it can be involved with real, quaternion, or hypercomplex-valued models, generalizing their adoption to single-channel data.
Hypercomplex Multimodal Emotion Recognition from EEG and Peripheral Physiological Signals
Multimodal emotion recognition from physiological signals is receiving an increasing amount of attention due to the impossibility to control them at will unlike behavioral reactions, thus providing more reliable information.
Dual Quaternion Rotational and Translational Equivariance in 3D Rigid Motion Modelling
To overcome these limitations, we employ a dual quaternion representation of rigid motions in the 3D space that jointly describes rotations and translations of point sets, processing each of the points as a single entity.
PHYDI: Initializing Parameterized Hypercomplex Neural Networks as Identity Functions
Neural models based on hypercomplex algebra systems are growing and prolificating for a plethora of applications, ranging from computer vision to natural language processing.
Attention-Map Augmentation for Hypercomplex Breast Cancer Classification
In this step, a parameterized hypercomplex neural network (PHNN) is employed to perform breast cancer classification.
Breast Cancer Histology Image Classification
Breast Tumour Classification
+3
Enhancing Semantic Communication with Deep Generative Models -- An ICASSP Special Session Overview
Semantic communication is poised to play a pivotal role in shaping the landscape of future AI-driven communication systems.
Generative Semantic Communication: Diffusion Models Beyond Bit Recovery
We prove, through an in-depth assessment of multiple scenarios, that our method outperforms existing solutions in generating high-quality images with preserved semantic information even in cases where the received content is significantly degraded.
StawGAN: Structural-Aware Generative Adversarial Networks for Infrared Image Translation
We test our model on aerial images of the DroneVeichle dataset containing RGB-IR paired images.
Hypercomplex Image-to-Image Translation
Image-to-image translation (I2I) aims at transferring the content representation from an input domain to an output one, bouncing along different target domains.
Ranked #3 on
Image-to-Image Translation
on CelebA-HQ
Multi-View Hypercomplex Learning for Breast Cancer Screening
To overcome such limitations, in this paper, we propose a methodological approach for multi-view breast cancer classification based on parameterized hypercomplex neural networks.
Ranked #1 on
Cancer-no cancer per breast classification
on InBreast
(using extra training data)
Dual Quaternion Ambisonics Array for Six-Degree-of-Freedom Acoustic Representation
We show that our dual quaternion SELD model with temporal convolution blocks (DualQSELD-TCN) achieves better results with respect to real and quaternion-valued baselines thanks to our augmented representation of the sound field.
Ranked #1 on
Sound Event Localization and Detection
on L3DAS21
PHNNs: Lightweight Neural Networks via Parameterized Hypercomplex Convolutions
In this paper, we define the parameterization of hypercomplex convolutional layers and introduce the family of parameterized hypercomplex neural networks (PHNNs) that are lightweight and efficient large-scale models.
Ranked #1 on
Sound Event Detection
on L3DAS21
Quaternion Generative Adversarial Networks
Latest Generative Adversarial Networks (GANs) are gathering outstanding results through a large-scale training, thus employing models composed of millions of parameters requiring extensive computational capabilities.
Ranked #1 on
Image Generation
on Oxford 102 Flowers 128x128
A Quaternion-Valued Variational Autoencoder
Deep probabilistic generative models have achieved incredible success in many fields of application.
