cornucopia 发表于 2025-3-26 21:00:42

http://reply.papertrans.cn/67/6636/663584/663584_31.png

cogitate 发表于 2025-3-27 05:02:00

http://reply.papertrans.cn/67/6636/663584/663584_32.png

Chagrin 发表于 2025-3-27 05:17:45

http://reply.papertrans.cn/67/6636/663584/663584_33.png

牢骚 发表于 2025-3-27 13:16:08

A Focally Discriminative Loss for Unsupervised Domain Adaptationised domain adaptation (UDA), where both domains follow different distributions, and the labels from source domain are merely available. However, MMD and its class-wise variants possibly ignore the intra-class compactness, thus canceling out discriminability of feature representation. In this paper,

senile-dementia 发表于 2025-3-27 15:33:15

Automatic Drum Transcription with Label Augmentation Using Convolutional Neural Networks The successful transcription of drum instruments is a key step in the analysis of drum music. Existing systems use the target drum instruments as a separate training objective, which faces the problems of over-fitting and limited performance improvement. To solve the above limitations, this paper p

RACE 发表于 2025-3-27 17:53:25

Adaptive Curriculum Learning for Semi-supervised Segmentation of 3D CT-Scanses a challenge which prevents deep learning models from obtaining the results they have achieved most especially in the field of medical imaging. Recently, self-training with deep learning has become a powerful approach to leverage labelled training and unlabelled data. However, a challenge of gener

性满足 发表于 2025-3-27 23:00:13

Genetic Algorithm and Distinctiveness Pruning in the Shallow Networks for VehicleXf real data often brings up privacy and data security issues. This paper aims to build a shallow neural network model for the pre-trained synthetic feature dataset, VehicleX. Using genetic algorithm to reduce the dimensional complexity by randomly selecting a subset of features from before training.

过份好问 发表于 2025-3-28 06:02:05

Stack Multiple Shallow Autoencoders into a Strong One: A New Reconstruction-Based Method to Detect A input from high-level features extracted from the samples. The underlying assumption of these methods is that a deep model trained on normal data would produce higher reconstruction error for abnormal input. But this underlying assumption is not always valid. Because the neural networks have a stro

GRIEF 发表于 2025-3-28 07:55:47

http://reply.papertrans.cn/67/6636/663584/663584_39.png

Peculate 发表于 2025-3-28 12:31:46

http://reply.papertrans.cn/67/6636/663584/663584_40.png
页: 1 2 3 [4] 5 6 7
查看完整版本: Titlebook: Neural Information Processing; 28th International C Teddy Mantoro,Minho Lee,Achmad Nizar Hidayanto Conference proceedings 2021 Springer Nat