Zlin’s Extraordinary Research Oasis

Zeal, Excellence, Reliability and Openness

Welcome to the ZERO Lab, the research group lead by Prof. Zhouchen Lin (Zlin), affiliated to School of Electronics Engineering and Computer Science, Peking University. We research on machine learning and computer vision.


  • Our group at Peking University is recruiting tenure-track faculties and PostDocs (academic or industrial). For the postdoc program in 2020, please refer to the Chinese version or the English version.
  • Our group at Peking University is recruiting Ph.D.s who have strong mathematical abilities (however, this does not imply that you have to come from mathematics department) and great interest in theoretical analysis in order to enjoy with me how to use mathematics to solve real problems elegantly.
  • Our group at Peking University is also recruiting Masters who have strong coding skills and interests in Quantitative Trading (a reference book). Welcome to send me your detailed resume!
  • Our group at Zhijiang LAB is recruiting Researchers and PostDocs.
  • Samsung AI Lab is recruiting top-caliber researchers with strong expertise in machine learning!


Acceleration ADMM Adversarial Robustness Adversarial Transferability Alternating direction method Alternating Direction Method of Multipliers Bayes error Boosting Color Filter Array Compressed Sensing Compressive Phase Retrieval, computer vision Contextual distance Convergence Analysis Convex Optimization Data Compression Deep Learning Demosaicking Denoising Dictionary Learning Dimensionality reduction Discriminant analysis Document analysis Double quantization Expectation Maximization Face recognition Feature detection Feature extraction forgery Geometric Optimization Handwriting recognition Image Annotation Image classification Image Denoising Image Processing Image Reconstruction Image rectification Image restoration Image Retrieval Image segmentation Laplace equations Learning-based PDEs Light field Linear discriminant analysis Lorentzian geometry Low Rank Low Rank Representation Low-level vision Lumigraph Machine Learning Majorization Minimization Manifold learning Manifolds Matrix Completion matrix decomposition Metric learning Mismatch Removal Neural Network Neural networks Nonconvex Optimization Optimal control Optimization Partial Differential Equation plenoptic functions Pose Estimation Principal component analysis Robust PCA Robust Principal Component Analysis sampling Semantic segmentation Semi-supervised learning Singular value decomposition Sparse Coding Sparse matrices Sparse Representation Spectral clustering Subspace clustering Subspace Recovery Super-Resolution Superresolution

Recentest Publications

Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State. NeurIPS, 2021.

Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient implementation on neuromorphic hardware. However, …

Reparameterized Sampling for Generative Adversarial Networks. ECML-PKDD, 2021.

Recently, sampling methods have been successfully applied to enhance the sample quality of Generative Adversarial Networks (GANs). …

Demystifying Adversarial Training via A Unified Probabilistic Framework. ICML workshop 2021, 2021.

Adversarial Training (AT) is known as an effective approach to enhance the robustness of deep neural networks. Recently researchers …

Towards Improving the Consistency, Efficiency, and Flexibility of Differentiable Neural Architecture Search. CVPR, 2021.

Most differentiable neural architecture search methods construct a super-net for search and derive a target-net as its sub-graph for …

PointFlow: Flowing Semantics Through Points for Aerial Image Segmentation. CVPR, 2021.

Aerial Image Segmentation is a particular semantic segmentation problem and has several challenging characteristics that general …

Gauge Equivariant Transformer. NeurIPS, 2021.

Attention mechanism has shown great performance and efficiency in a lot of deep learning models, in which relative position encoding …

Dissecting the Diffusion Process in Linear Graph Convolutional Networks. NeurIPS, 2021.

Graph Convolutional Networks (GCNs) have attracted more and more attentions in recent years. A typical GCN layer consists of a linear …

Residual Relaxation for Multi-view Representation Learning. NeurIPS, 2021.

Multi-view methods learn representations by aligning multiple views of the same image and their performance largely depends on the …


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