Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2023; 32nd International C Lazaros Iliadis,Antonios Papaleonidas,Chrisina Jay Confe

華而不實(shí)

,K-Fold Cross-Valuation for?Machine Learning Using Shapley Value,d and the volume of data, we propose the Monte Carlo permutation, incremental learning, and batch data valuation methodologies. This approach aids in approximating the true Shapley value as precisely as possible while simultaneously reducing computation time. Extensive experiments have demonstrated

減弱不好

樸素

nitroglycerin

富饒

,SS-Net: 3D Spatial-Spectral Network for?Cerebrovascular Segmentation in?TOF-MRA,bution patterns of cerebrovascular edges more effectively. Experimental results show that the SS-Net delivers outstanding performance, achieving the DSC of 71.14% on a publicly available dataset and outperforming other 3D deep-learning-based approaches. Code: github.com/y8421036/SS-Net.

hallow

會(huì)犯錯(cuò)誤

,Style Expansion Without Forgetting for?Handwritten Character Recognition,istillation and replay, CPA learns representative information by memorizing character-representative prototypes and augmenting them in new learning phases to better distinguish different characters when the replay data is limited, and SGM augments the prototypes in a reliable way to improves the rel

EXUDE

不斷的變動(dòng)

,UG-Net: Unsupervised-Guided Network for?Biomedical Image Segmentation and?Classification,ssification network for accurate classification. Moreover, a novel contextual encoding module is presented to capture high-level information and preserve spatial information. Meanwhile, a hybrid loss is defined to alleviate the imbalance training problem. Experimental results show that our approach

儀式

,Visible-Infrared Person Re-identification via?Modality Augmentation and?Center Constraints,e modality discrepancy and, to some extent, alleviates the modality imbalance problem. In addition, based on the idea of partition, we design a fine-grained feature mining module (FFMM) to mine nuanced but discriminative information within each part, which is benefit to further alleviate the modalit