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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
SOFTMATCH: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning
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)
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
Improve the recently-proposed “MixMatch” semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring (2023/02/21)
SPICE: Semantic Pseudo-Labeling for Image Clustering
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)
Self-Adaptive Multiprototype-Based Competitive Learning Approach: A k-Means-Type Algorithm for Imbalanced Data Clustering
Use multiple subclusters to represent each cluster with an automatic adjustment of the number of subclusters (2023/06/26)
Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data
Propose two novel techniques: Entropy Meaning Loss (EML) and Adaptive Negative Learning (ANL) to better leverage all unlabeled data (2023/07/31)
Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation
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)
PEFAT: Boosting Semi-supervised Medical Image Classification via Pseudo-loss Estimation and Feature Adversarial Training
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)
Propagation Regularizer for Semi-supervised Learning with Extremely Scarce Labeled Samples
Propose a propagation regularizer which can achieve efficient and effective learning with extremely scarce labeled samples by suppressing confirmation bias (2023/08/19)
On Non-random Missing Labels in Semi-supervised Learning
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)
ACPL: Anti-curriculum Pseudo-labelling for Semi-supervised Medical Image Classification
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)
Towards Semi-supervised Learning with Non-random Missing Labels
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)
FREEMATCH: Self-adaptive Thresholding For Semi-supervised Learning
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)
Further explanation of FreeMatch’s fairness technique
A review of information theory and its different uses when used in loss functions (2023/10/15)
portfolioprevious
(Overview) A Survey on Deep Semi-supervised Learning
Semi-supervised learning methods are divided into four categories and some methods are selected and introduced respectively (2022/10/12)
Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling
Propose a unified sampling framework, which significantly boosts the balancedness and accuracy of contrastive learning via strategically sampling additional data (2022/10/19)
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning
Use unsupervised learning methods to solve the effective selection problem of labeled samples in semi-supervised learning (2022/11/01)
RDA: Reciprocal Distribution Alignment for Robust Semi-Supervised Learning
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)
An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning
Supplement the infrequent classes with more pseudo-labels and frequent classes with less pseudo-labels after each training epoch (2022/12/06)
Exploring Balanced Feature Spaces for Representation Learning
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)
publications
Robust retrieval of material chemical states in X-ray microspectroscopy
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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talks
Imaging With Equivariant Deep Learning
Published:
Introduced the concept of equivariant and confirmed the role of equivariant in deep learning network construction and learning. link
Generative Models for Inverse Imaging Problems From mathematical foundations to physics-driven applications
Published:
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
Physics-Embedded Machine Learning for Electromagnetic Data Imaging
Published:
Introduced three modes of EM image processing, and focused on the unrolling-based method of physics-embedded model.link
Physics-Driven Deep Learning Methods for Fast Quantitative Magnetic Resonance Imaging
Published:
Introduced qMRI and the applications of three kinds of physics-driven learning methods. link
Tensors for Deep Learning theory
Published:
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
Operational Research and Optimization
Teaching Assistant, SUSTech, Department of Statistics and Data Science, 2023
Discrete Mathematics
Teaching Assistant, SUSTech, Department of Statistics and Data Science, 2023