Feb 11, 2022 · Our method aims to extract an explicit weighting function with sample loss and task/class feature as input, and sample weight as output.

The learned weighting scheme can be used in a plug-and-play manner, and can be directly deployed on unseen datasets, without need to specifically extra tune ...

TPAMI2023: CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning (Official Pytorch implementation).

Sep 5, 2023 · The learned weighting scheme can be used in a plug-and-play manner, and can be directly deployed on unseen datasets, without need to.

A meta-model capable of adaptively learning an explicit weighting scheme directly from data is proposed, by seeing each training class as a separate learning ...

Jun 7, 2024 · Compared to MW-Net, CMW-Net [52] is more refined and class-based. The adaptive change weights of different sample classes of the target model ...

A class-aware sample weighting algorithm is developed for general label noise problems. The algorithm can effectively tackle complicated and diverse noisy ...

Auto^6ML is a open-source library for machine learning automation. It is based entirely on jittor, offering high performance and faster speeds.

Cmw-net: Learning a class-aware sample weighting mapping for robust deep learning. J Shu, X Yuan, D Meng, Z Xu. IEEE Transactions on Pattern Analysis and ...

CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning ... weighting function with sample loss and task/class feature as input ...