Propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training, effectively exploiting the unlabeled data (2023/02/14)
Improve the recently-proposed “MixMatch” semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring (2023/02/21)
Design two semantics-aware pseudo-labeling algorithms, prototype pseudo-labeling and reliable pseudo-labeling, which enable accurate and reliable self-supervision over clustering (2023/03/06)
Use multiple subclusters to represent each cluster with an automatic adjustment of the number of subclusters (2023/06/26)
Propose two novel techniques: Entropy Meaning Loss (EML) and Adaptive Negative Learning (ANL) to better leverage all unlabeled data (2023/07/31)
Propose an auxiliary feature perturbation stream as a supplement, leading to an expanded perturbation space. In addition, to sufficiently probe original image-level augmentations, this paper presents a dual-stream perturbation technique (2023/08/06)
Develop a data selection scheme to split a high-quality pseudo-labeled set. For low-quality pseudo-labels, this paper presents a regularization approach to learn discriminate information from them via injecting adversarial noises at the feature-level (2023/08/14)
Propose a propagation regularizer which can achieve efficient and effective learning with extremely scarce labeled samples by suppressing confirmation bias (2023/08/19)
Propose Class-Aware Propensity (CAP) score that exploits the unlabeled data to train an improved classifier using the biased labeled data. Furthermore, this paper proposes Class-Aware Imputation (CAI) that dynamically decreases (or increases) the pseudo-label assignment threshold for rare (or frequent) classes (2023/09/01)
Select informative unlabelled samples, improving training balance and allowing the model to work for both multi-label and multi-class problems, and to estimate pseudo labels by an accurate ensemble of classifiers (2023/09/04)
Explore the class-level guidance information obtained by the Markov random walk, which is modeled on a dynamically created graph built over the class tracking matrix (2023/09/16)
Adjust the confidence threshold in a self-adaptive manner according to the model’s learning status. Further, this paper introduces a self-adaptive class fairness regularization penalty to encourage the model for diverse predictions during the early training stage (2023/09/26)
A review of information theory and its different uses when used in loss functions (2023/10/15)
Semi-supervised learning methods are divided into four categories and some methods are selected and introduced respectively (2022/10/12)
Propose a unified sampling framework, which significantly boosts the balancedness and accuracy of contrastive learning via strategically sampling additional data (2022/10/19)
Use unsupervised learning methods to solve the effective selection problem of labeled samples in semi-supervised learning (2022/11/01)
Enforce a reciprocal alignment on the distributions of the predictions from two classifiers predicting pseudo-labels and complementary labels on the unlabeled data (2022/11/17)
Supplement the infrequent classes with more pseudo-labels and frequent classes with less pseudo-labels after each training epoch (2022/12/06)
Conduct a series of studies on the performance of self-supervised contrastive learning and supervised learning methods over multiple datasets where training instance distributions vary from a balanced one to a long-tailed one (2022/12/27)
Published in Optics Express, 2023
We propose a novel data formulation model for X-ray microspectroscopy and develop a dedicated unmixing framework to solve this problem, which is robust to noise and spectral variability. Moreover, this framework is not limited to the analysis of two-state material chemistry, making it an effective alternative to conventional and widely-used methods. In addition, an alternative directional multiplier method with provable convergence is applied to obtain the solution efficiently.
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Published:
Introduced the concept of equivariant and confirmed the role of equivariant in deep learning network construction and learning. link
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Introduced the classic generative model foundations VAEs, GANs and Diffusion Model, and then used these theories to explain physics-driven applications of generative models. link
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Introduced three modes of EM image processing, and focused on the unrolling-based method of physics-embedded model.link
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Introduced qMRI and the applications of three kinds of physics-driven learning methods. link
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The rank based on the grid tensor matrix is used as the lower bound of the separation rank, and the theory of neural network is explained from the perspective of rank, including depth, width, and bottleneck. link
Teaching Assistant, SUSTech, Department of Statistics and Data Science, 2023
Teaching Assistant, SUSTech, Department of Statistics and Data Science, 2023