27 papers · ranked by Valyu relevance
Linus Ericsson, Henry Gouk, Chen Change Loy, Timothy M. Hospedales
—Self-supervised representation learning methods aim to provide powerful deep feature learning without the requirement of large annotated datasets, thus alleviating the annotation bottleneck that is one of the main barriers to practical deployment of deep learning today. These methods have advanced rapidly in recent…
Dimitris Spathis, Ignacio Perez-Pozuelo, Laia Marques-Fernandez, Cecilia Mascolo
Title: Summary Medicine is undergoing an unprecedented digital transformation, as massive amounts of health data are being produced, gathered, and curated, ranging from in-hospital (e.g., intensive care unit [ICU]) to person-generated data (wearables). Annotating all these data for training purposes in order to feed to…
Yinjun Jia, Shuaishuai Li, Xuan Guo, Junqiang Hu + 2 more
Fast and accurately characterizing animal behaviors is crucial for neuroscience research. Deep learning models are efficiently used in laboratories for behavior analysis. However, it has not been achieved to use a fully unsupervised method to extract comprehensive and discriminative features directly from raw behavior…
Shih-Cheng Huang, Anuj Pareek, Malte Jensen, Matthew P. Lungren + 2 more
'Serena Yeung' 'Akshay S. Chaudhari'] Advancements in deep learning and computer vision provide promising solutions for medical image analysis, potentially improving healthcare and patient outcomes. However, the prevailing paradigm of training deep learning models requires large quantities of labeled training data…
Kevin G. Montero Quispe, Daniel M. S. Utyiama, Eulanda M. dos Santos, Horácio A. B. F. Oliveira + 2 more
'Horácio A. B. F. Oliveira' 'Eduardo J. P. Souto' 'Mario Munoz-Organero'] The use of machine learning (ML) techniques in affective computing applications focuses on improving the user experience in emotion recognition. The collection of input data (e.g., physiological signals), together with expert annotations are part…
Taoran Sheng, Manfred Huber, Giovanni Saggio
Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costly to obtain.…
Yi Wang, Conrad M Albrecht, Nassim Ait Ali Braham, Lichao Mou + 1 more
'Xiao Xiang Zhu'] Abstract—In deep learning research, self-supervised learning (SSL) has received great attention triggering interest within both the computer vision and remote sensing communities. While there has been a big success in computer vision, most of the potential of SSL in the domain of earth observation…
Markus Marks, Manuel Knott, Neehar Kondapaneni, Elijah Cole + 3 more
'Thijs Defraeye' 'Fernando Perez-Cruz' 'Pietro Perona'] Self-supervised learning (SSL) is a machine learning approach where the data itself provides supervision, eliminating the need for external labels. The model is forced to learn about the data’s inherent structure or context by solving a pretext task. With SSL…
Qing Chang, Junran Peng, Lingxie Xie, Jiajun Sun + 3 more
'Qi Tian' 'Zhaoxiang Zhang'] Qing Chang 1,3,4,5 Junran Peng 2 Lingxi Xie 2 Jiajun Sun 1,3,4,5 Haoran Yin 1,3,4,5 Qi Tian 2 Zhaoxiang Zhang*1,3,4,5,6 1University of Chinese Academy of Sciences, 2Huawei Inc. 3 Institute of Automation, Chinese Academy of Sciences 4National Laboratory of Pattern Recognition 5Center for…
Idan Daniel Grosbard, Galit Yovel
With the rapid development of Artificial Neural Network based visual models, many studies have shown that these models show unprecedented potence in predicting neural responses to images in visual cortex. Lately, advances in computer vision have introduced self-supervised models, where a model is trained using…
Asaki Kataoka, Yoshihiro Nagano, Masafumi Oizumi
Recent advances in self-supervised learning have attracted significant attention from both machine learning and neuroscience. This is primarily because self-supervised methods do not require annotated supervisory information, making them applicable to training artificial networks without relying on large amounts of…
Lee, Byeongchan
Self-supervised representation learning has achieved impressive empirical success, yet its theoretical understanding remains limited. In this work, we provide a theoretical perspective by formulating self-supervised representation learning as an approximation to supervised representation learning objectives. Based on…
Niall Rodgers
Palaeontology has seen widespread and growing use of machine learning to classify and analyse large datasets of fossils. However, palaeontology is a challenging field in which to apply machine learning. Datasets may be small or unlabelled, images may be complex and different from standard datasets and palaeontologists…
Rahil Gholamipoor, Nima Rafiee, Markus Kollmann
X-ray images have been widely used for medical diagnoses of cardiothoracic and pulmonary abnormalities due to its noninvasiveness. Advancement in computer-aided diagnostic technologies, such as deep supervised methods, can help radiologists with a reliable early treatment and reduce diagnosis time. Nevertheless, these…
Levy Chaves, Alceu Bissoto, Eduardo Valle, Sandra Avila
Self-supervised pre-training appears as an advantageous alternative to supervised pre-trained for transfer learning. By synthesizing annotations on pretext tasks, self-supervision allows pre-training models on large amounts of pseudo-labels before fine-tuning them on the target task. In this work, we assess…
Adriano Vinhas, João Correia, Penousal Machado
Self-Supervised learning Authors: ['Adriano Vinhas' 'João Correia' 'Penousal Machado'] Abstract— Deep Neural Networks (DNNs) have been successfully applied to a wide range of problems. However, two main limitations are commonly pointed out. The first one is that they require long time to design. The other is that they…
Kevin Kermani Nejad, Paul Anastasiades, Loreen Hertäg, Rui Ponte Costa
The neocortex constructs an internal representation of the world, but the underlying circuitry and computational principles remain unclear. Inspired by self-supervised learning algorithms, we introduce a computational model wherein layer 2/3 (L2/3) learns to predict incoming sensory stimuli by comparing previous…
Jonah Anton, Liam Castelli, Mun Fai Chan, Mathilde Outters + 10 more
'Wan Hee Tang' 'Venus Cheung' 'Pancham Shukla' 'Rahee Walambe' 'Ketan Kotecha' 'Cecilia Di Ruberto' 'Alessandro Stefano' 'Albert Comelli' 'Lorenzo Putzu' 'Andrea Loddo'] Self-supervised learning approaches have seen success transferring between similar medical imaging datasets, however there has been no large scale…
Daniel Otero, Rafael Mateus
Sewerage infrastructure is among the most expensive modern investments requiring time-intensive manual inspections by qualified personnel. Our study addresses the need for automated solutions without relying on large amounts of labeled data. We propose a novel application of Self-Supervised Learning (SSL) for sewer…
Hongyang Dong, Simon D.M. Jacques, Keith T. Butler, Olof Gutowski + 4 more
In this study, we introduce a method designed to eliminate parallax artefacts present in Xray powder diffraction computed tomography data acquired from large samples. These parallax artefacts manifest as artificial peak shifting, broadening and splitting, leading to inaccurate physicochemical information, such as…
Authors not listed
Machine learning approaches for conceptualizing and designing in silico compounds have attracted significant attention. However, the applicability of these compounds is often challenged by synthetic viability and cost-effectiveness. Researchers introduced proxy-scores, known as synthethic accessiblity scoring, to…
Jong-Yeup Kim, Gayrat Tangriberganov, Woochul Jung, Dae Sung Kim + 3 more
In order to reach better performance in visual representation learning from image or video dataset, huge amount of annotated data are on demand. However, collecting and annotating large-scale datasets are costly and time-consuming tasks. Especially, in a domain like medical, it is hard to access patient images because…
Hongyang Dong, Simon D.M. Jacques, Winfried Kockelmann, Stephen W. T. Price + 10 more
Hongyang Dong 3 , Simon D.M. Jacques 1 , Winfried Kockelmann 4 , Stephen W. T. Price 1 , Robert Emberson 5 , Dorota Matras 6,7 , Yaroslav Odarchenko 1 , Vesna Middelkoop 10 , Athanasios Giokaris 1 , Olof Gutowski 8 , Ann-Christin Dippel 8 , Martin v. Zimmermann 8 , Andrew M. Beale 3 , Keith T. Butler 9 , Antonis…
Authors not listed
nDTomo is a Python-based software suite for the simulation, reconstruction and analysis of X-ray chemical imaging and computed tomography data. It provides a collection of Python function-based tools designed for accessibility and education as well as a graphical user interface (GUI). Prioritising transparency and ease…
Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi + 3 more
Massive scale, both in terms of data availability and computation, enables significant breakthroughs in key application areas of deep learning such as natural language processing (NLP) and computer vision. There is emerging evidence that scale may be a key ingredient in scientific deep learning, but the importance of…
Authors not listed
Machine learning models are increasingly applied to heterogeneous materials datasets spanning different synthesis routes, measurement protocols, and structural classes. Although multi-task and representation-learning approaches are commonly used to improve predictive performance, the latent representations learned by…
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…