18 papers · ranked by Valyu relevance
Viktoria Schuster, Anders Krogh, Valentina Boeva
For the image-generating DGD, we choose a network architecture based on convolutions and tricks from other image generation models and image segmentation techniques (, , ). The decoder starts with two fully connected hidden layers fed with the latent representations. The first hidden layer has 100 units, the second…
Fangping Wan, Daphne Kontogiorgos-Heintz, Cesar de la Fuente-Nunez
Computers can already be programmed for superhuman pattern recognition of images and text. For machines to discover novel molecules, they must first be trained to sort through the many characteristics of molecules and determine which properties should be retained, suppressed, or enhanced to optimize functions of…
Mengran Yan, Chun Tang, Jida Yan, Siti Suhaily Surip + 1 more
Pattern design is essential in various domains, especially in traditional lantern production, where patterns convey cultural history and artistic values. Our research presents an innovative generative model that produces customizable lantern patterns, integrating classical aesthetics with modern design features via a…
Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan, Cecilia Di Ruberto + 4 more
Deep learning has become a popular tool for medical image analysis, but the limited availability of training data remains a major challenge, particularly in the medical field where data acquisition can be costly and subject to privacy regulations. Data augmentation techniques offer a solution by artificially increasing…
Bing Du, Xiaomu Cheng, Yiping Duan, Huansheng Ning + 5 more
'Andrea Luigi Guerra' 'Gabriele Baronio' 'Domenico Speranza' 'Luca Ulrich'] Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain-computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and…
Minjoo Kim, Yelim Kim, Won Il Park
This study introduces an optical neural network (ONN)-based autoencoder for efficient image processing, utilizing specialized optical matrix-vector multipliers for both encoding and decoding tasks. To address the challenges in efficient decoding, we propose a method that optimizes output processing through scalar…
Yanming Zhu, Min Wang, Xuefei Yin, Jue Zhang + 3 more
'Jiankun Hu' 'Yang Yue'] Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have…
Dagao Duan, Qiuli Zhang, Zhongming Han, Haitao Xiong
Natural language generation (NLG) is a core component of machine translation, dialogue systems, speech recognition, summarization, and so forth. The existing text generation methods tend to be based on recurrent neural language models (NLMs), which generate sentences from encoding vector. However, most of these models…
Aman Singh, Tokunbo Ogunfunmi, Sotiris Kotsiantis
Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with…
Romain Lopez, Adam Gayoso, Nir Yosef
Generative models provide a well-established statistical framework for evaluating uncertainty and deriving conclusions from large data sets especially in the presence of noise, sparsity, and bias. Initially developed for computer vision and natural language processing, these models have been shown to effectively…
Vignesh Sampath, Iñaki Maurtua, Juan José Aguilar Martín, Aitor Gutierrez
Any computer vision application development starts off by acquiring images and data, then preprocessing and pattern recognition steps to perform a task. When the acquired images are highly imbalanced and not adequate, the desired task may not be achievable. Unfortunately, the occurrence of imbalance problems in…
Benchen Yang, Xuzhao Liu, Yize Li, Haibo Jin + 2 more
Unpaired image-to-image translation (I2IT) involves establishing an effective mapping between the source and target domains to enable cross-domain image transformation. Previous contrastive learning methods inadequately accounted for the variations in features between two domains and the interrelatedness of elements…
Yuling He, Yingding Zhao, Wenji Yang, Yilu Xu + 1 more
'Juan Pedro Dominguez-Morales'] Due to the sophisticated entanglements for non-rigid deformation, generating person images from source pose to target pose is a challenging work. In this paper, we present a novel framework to generate person images with shape consistency and appearance consistency. The proposed…
Shiqi Chen, Yuhang Li, Yuntian Wang, Hanlong Chen + 1 more
Generative models cover various application areas, including image and video synthesis, natural language processing and molecular design, among many others1-11. As digital generative models become larger, scalable inference in a fast and energy-efficient manner becomes a challenge12-14. Here we present optical…
Kiran Bacsa, Zhilu Lai, Wei Liu, Michael Todd + 1 more
We propose a new variational autoencoder (VAE) with physical constraints capable of learning the dynamics of Multiple Degree of Freedom (MDOF) dynamic systems. Standard variational autoencoders place greater emphasis on compression than interpretability regarding the learned latent space. We propose a new type of…
Saleh Albelwi, Gholamreza Anbarjafari
Although deep learning algorithms have achieved significant progress in a variety of domains, they require costly annotations on huge datasets. Self-supervised learning (SSL) using unlabeled data has emerged as an alternative, as it eliminates manual annotation. To do this, SSL constructs feature representations using…
Bilal Ahmad, Sun Jun, Vasile Palade, Qi You + 6 more
Deep learning has gained immense attention from researchers in medicine, especially in medical imaging. The main bottleneck is the unavailability of sufficiently large medical datasets required for the good performance of deep learning models. This paper proposes a new framework consisting of one variational…
Gwangwoo Kim, Hyonho Chun
Background Deep generative models naturally become nonlinear dimension reduction tools to visualize large-scale datasets such as single-cell RNA sequencing datasets for revealing latent grouping patterns or identifying outliers. The variational autoencoder (VAE) is a popular deep generative method equipped with…