Results 51 to 60 of about 97,243 (265)

Backdoor Attacks on Self-Supervised Learning

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Large-scale unlabeled data has spurred recent progress in self-supervised learning methods that learn rich visual representations. State-of-the-art self-supervised methods for learning representations from images (e.g., MoCo, BYOL, MSF) use an inductive bias that random augmentations (e.g., random crops) of an image should produce similar embeddings ...
Aniruddha Saha, Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Hamed Pirsiavash   +3 more
openaire   +2 more sources

Self-Supervised Learning for Solar Radio Spectrum Classification

Universe, 2022
Solar radio observation is an important way to study the Sun. Solar radio bursts contain important information about solar activity. Therefore, real-time automatic detection and classification of solar radio bursts are of great value for subsequent solar
Siqi Li, Guowu Yuan, Jian Chen, Chengming Tan, Hao Zhou   +4 more
doaj   +1 more source

Self-Supervised Learning with Swin Transformers

CoRR, 2021
We are witnessing a modeling shift from CNN to Transformers in computer vision. In this work, we present a self-supervised learning approach called MoBY, with Vision Transformers as its backbone architecture. The approach basically has no new inventions, which is combined from MoCo v2 and BYOL and tuned to achieve reasonably high accuracy on ImageNet ...
Zhenda Xie, Yutong Lin, Zhuliang Yao, Zheng Zhang 0022, Qi Dai 0001, Yue Cao 0001, Han Hu 0001   +6 more
openaire   +2 more sources

The Challenges of Continuous Self-Supervised Learning

, 2022
Self-supervised learning (SSL) aims to eliminate one of the major bottlenecks in representation learning - the need for human annotations. As a result, SSL holds the promise to learn representations from data in-the-wild, i.e., without the need for finite and static datasets.
Senthil Purushwalkam, Pedro Morgado 0001, Abhinav Gupta 0001   +2 more
openaire   +2 more sources

Improving drug–target affinity prediction by adaptive self-supervised learning [PDF]

PeerJ Computer Science
Computational drug-target affinity prediction is important for drug screening and discovery. Currently, self-supervised learning methods face two major challenges in drug-target affinity prediction.
Qing Ye, Yaxin Sun
doaj   +2 more sources

Experimental Case Study of Self-Supervised Learning for Voice Spoofing Detection

IEEE Access, 2023
This study aims to improve the performance of voice spoofing attack detection through self-supervised pre-training. Supervised learning needs appropriate input variables and corresponding labels for constructing the machine learning models that are to be
Yerin Lee, Narin Kim, Jaehong Jeong, Il-Youp Kwak   +3 more
doaj   +1 more source

Self-supervised Learning by View Synthesis

CoRR, 2023
We present view-synthesis autoencoders (VSA) in this paper, which is a self-supervised learning framework designed for vision transformers. Different from traditional 2D pretraining methods, VSA can be pre-trained with multi-view data. In each iteration, the input to VSA is one view (or multiple views) of a 3D object and the output is a synthesized ...
Shaoteng Liu, Xiangyu Zhang 0005, Tao Hu 0011, Jiaya Jia   +3 more
openaire   +2 more sources

X-ray modalities in the era of artificial intelligence: overview of self-supervised learning approach

FACETS
Self-supervised learning enables the creation of algorithms that outperform supervised pre-training methods in numerous computer vision tasks. This paper provides a comprehensive overview of self-supervised learning applications across various X-ray ...
Ivan Martinović, Shitong Mao, Mehdy Dousty, Wuqi Li, Milena Đukanović, Errol Colak, Ervin Sejdić   +6 more
doaj   +1 more source

Self-Supervised Learning for the Distinction between Computer-Graphics Images and Natural Images

Applied Sciences, 2023
With the increasing visual realism of computer-graphics (CG) images generated by advanced rendering engines, the distinction between CG images and natural images (NIs) has become an important research problem in the image forensics community.
Kai Wang
doaj   +1 more source

Discovering Lin-Kernighan-Helsgaun heuristic for routing optimization using self-supervised reinforcement learning

Journal of King Saud University: Computer and Information Sciences, 2023
Vehicle routing optimization is a crucial responsibility of transportation service providers, which can significantly reduce operating expenses and improve client satisfaction.
Qi Wang, Chengwei Zhang, Chunlei Tang
doaj   +1 more source