Search Results for author: Renzhen Wang

Found 9 papers, 5 papers with code

Meta Feature Modulator for Long-tailed Recognition

no code implementations8 Aug 2020 Renzhen Wang, Kaiqin Hu, Yanwen Zhu, Jun Shu, Qian Zhao, Deyu Meng

We further design a modulator network to guide the generation of the modulation parameters, and such a meta-learner can be readily adapted to train the classification network on other long-tailed datasets.

General Classification Meta-Learning +1

Unsupervised Learning of Local Discriminative Representation for Medical Images

1 code implementation17 Dec 2020 Huai Chen, Jieyu Li, Renzhen Wang, YiJie Huang, Fanrui Meng, Deyu Meng, Qing Peng, Lisheng Wang

However, the commonly applied supervised representation learning methods require a large amount of annotated data, and unsupervised discriminative representation learning distinguishes different images by learning a global feature, both of which are not suitable for localized medical image analysis tasks.

Clustering Representation Learning

Residual Moment Loss for Medical Image Segmentation

no code implementations27 Jun 2021 Quanziang Wang, Renzhen Wang, Yuexiang Li, Kai Ma, Yefeng Zheng, Deyu Meng

Location information is proven to benefit the deep learning models on capturing the manifold structure of target objects, and accordingly boosts the accuracy of medical image segmentation.

Image Segmentation Medical Image Segmentation +2

Unsupervised Local Discrimination for Medical Images

1 code implementation21 Aug 2021 Huai Chen, Renzhen Wang, Xiuying Wang, Jieyu Li, Qu Fang, Hui Li, Jianhao Bai, Qing Peng, Deyu Meng, Lisheng Wang

To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images.

Contrastive Learning Lesion Segmentation +2

Label Hierarchy Transition: Delving into Class Hierarchies to Enhance Deep Classifiers

1 code implementation4 Dec 2021 Renzhen Wang, De Cai, Kaiwen Xiao, Xixi Jia, Xiao Han, Deyu Meng

Existing methods commonly address hierarchical classification by decoupling it into a series of multi-class classification tasks.

Classification Multi-class Classification +1

Relational Experience Replay: Continual Learning by Adaptively Tuning Task-wise Relationship

no code implementations31 Dec 2021 Quanziang Wang, Renzhen Wang, Yuexiang Li, Dong Wei, Kai Ma, Yefeng Zheng, Deyu Meng

Continual learning is a promising machine learning paradigm to learn new tasks while retaining previously learned knowledge over streaming training data.

Continual Learning Meta-Learning

Imbalanced Semi-supervised Learning with Bias Adaptive Classifier

1 code implementation28 Jul 2022 Renzhen Wang, Xixi Jia, Quanziang Wang, Yichen Wu, Deyu Meng

The core idea is to automatically assimilate the training bias caused by class imbalance via the bias adaptive classifier, which is composed of a novel bias attractor and the original linear classifier.

CBA: Improving Online Continual Learning via Continual Bias Adaptor

1 code implementation ICCV 2023 Quanziang Wang, Renzhen Wang, Yichen Wu, Xixi Jia, Deyu Meng

Online continual learning (CL) aims to learn new knowledge and consolidate previously learned knowledge from non-stationary data streams.

Continual Learning

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