Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Long, Xianzhong"'
Prompt treatment for melanoma is crucial. To assist physicians in identifying lesion areas precisely in a quick manner, we propose a novel skin lesion segmentation technique namely SLP-Net, an ultra-lightweight segmentation network based on the spiki
Externí odkaz:
http://arxiv.org/abs/2312.12789
In contrastive self-supervised learning, positive samples are typically drawn from the same image but in different augmented views, resulting in a relatively limited source of positive samples. An effective way to alleviate this problem is to incorpo
Externí odkaz:
http://arxiv.org/abs/2311.00562
Contrastive learning predicts whether two images belong to the same category by training a model to make their feature representations as close or as far away as possible. In this paper, we rethink how to mine samples in contrastive learning, unlike
Externí odkaz:
http://arxiv.org/abs/2311.00358
The popularity of self-supervised learning has made it possible to train models without relying on labeled data, which saves expensive annotation costs. However, most existing self-supervised contrastive learning methods often overlook the combinatio
Externí odkaz:
http://arxiv.org/abs/2306.15930
Self-supervised methods based on contrastive learning have achieved great success in unsupervised visual representation learning. However, most methods under this framework suffer from the problem of false negative samples. Inspired by the mean shift
Externí odkaz:
http://arxiv.org/abs/2305.05370
Contrastive learning has emerged as an essential approach for self-supervised learning in visual representation learning. The central objective of contrastive learning is to maximize the similarities between two augmented versions of an image (positi
Externí odkaz:
http://arxiv.org/abs/2304.02971
Publikováno v:
In Knowledge-Based Systems 5 September 2024 299
Publikováno v:
In Engineering Applications of Artificial Intelligence March 2024 129
Publikováno v:
In Knowledge-Based Systems 11 January 2024 283
Many works demonstrate that deep learning system is vulnerable to adversarial attack. A deep learning system consists of two parts: the deep learning task and the deep model. Nowadays, most existing works investigate the impact of the deep model on r
Externí odkaz:
http://arxiv.org/abs/2010.05125