Search Results for author: Mei Wang

Found 15 papers, 3 papers with code

Cycle Label-Consistent Networks for Unsupervised Domain Adaptation

no code implementations27 May 2022 Mei Wang, Weihong Deng

The cycle label-consistent loss reinforces the consistency between ground-truth labels and pseudo-labels of source samples leading to statistically similar latent representations between source and target domains.

Unsupervised Domain Adaptation

Deep face recognition with clustering based domain adaptation

no code implementations27 May 2022 Mei Wang, Weihong Deng

Despite great progress in face recognition tasks achieved by deep convolution neural networks (CNNs), these models often face challenges in real world tasks where training images gathered from Internet are different from test images because of different lighting condition, pose and image quality.

Domain Adaptation Face Recognition

Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms

1 code implementation19 May 2022 Mei Wang, Weihong Deng

We introduce the Oracle-MNIST dataset, comprising of 28$\times $28 grayscale images of 30, 222 ancient characters from 10 categories, for benchmarking pattern classification, with particular challenges on image noise and distortion.

BIG-bench Machine Learning

Meta Balanced Network for Fair Face Recognition

no code implementations13 May 2022 Mei Wang, Yaobin Zhang, Weihong Deng

Finally, to mitigate the algorithmic bias, we propose a novel meta-learning algorithm, called Meta Balanced Network (MBN), which learns adaptive margins in large margin loss such that the model optimized by this loss can perform fairly across people with different skin tones.

Face Recognition Meta-Learning

Unsupervised Structure-Texture Separation Network for Oracle Character Recognition

1 code implementation13 May 2022 Mei Wang, Weihong Deng, Cheng-Lin Liu

Second, transformation is achieved via swapping the learned textures across domains and a classifier for final classification is trained to predict the labels of the transformed scanned characters.

Disentanglement Unsupervised Domain Adaptation

Augmented Geometric Distillation for Data-Free Incremental Person ReID

no code implementations CVPR 2022 Yichen Lu, Mei Wang, Weihong Deng

On this basis, we reveal a "noisy distillation" problem stemming from the noise in dreaming memory, and further propose to augment distillation in a pairwise and cross-wise pattern over different views of memory to mitigate it.

Incremental Learning Person Re-Identification

Predicting Disease Progress with Imprecise Lab Test Results

no code implementations8 Jul 2021 Mei Wang, Jianwen Su, Zhihua Lin

In this method, each sample in imprecision range space has a certain probability to be the real value, participating in the loss calculation.

Impact of Medical Data Imprecision on Learning Results

no code implementations24 Jul 2020 Mei Wang, Jianwen Su, Haiqin Lu

In this paper, we initiate a study on the impact of imprecision on prediction results in a healthcare application where a pre-trained model is used to predict future state of hyperthyroidism for patients.

Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning

no code implementations CVPR 2020 Mei Wang, Weihong Deng

Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly.

Face Recognition Fairness +2

Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning

no code implementations25 Nov 2019 Mei Wang, Weihong Deng

To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses.

Face Recognition Fairness +2

Time-weighted Attentional Session-Aware Recommender System

no code implementations12 Sep 2019 Mei Wang, Weizhi Li, Yan Yan

Session-based Recurrent Neural Networks (RNNs) are gaining increasing popularity for recommendation task, due to the high autocorrelation of user's behavior on the latest session and the effectiveness of RNN to capture the sequence order information.

Collaborative Filtering Recommendation Systems

Deep Face Recognition: A Survey

6 code implementations18 Apr 2018 Mei Wang, Weihong Deng

Deep learning applies multiple processing layers to learn representations of data with multiple levels of feature extraction.

Face Recognition Face Verification +1

Deep Visual Domain Adaptation: A Survey

no code implementations10 Feb 2018 Mei Wang, Weihong Deng

Deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data.

Domain Adaptation Face Recognition +4

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