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 Artificial Intelligence, 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.


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

Latest Publications

Optimization-induced Implicit Graph Diffusion. ICML, 2022.

Due to the over-smoothing issue, most existing graph neural networks can only capture limited de- pendencies with their inherently …

Training Much Deeper Spiking Neural Networks with a Small Number of Time-Steps. Neural Networks, 2022.

Spiking Neural Network (SNN) is a promising energy-efficient neural architecture when implemented on neuromorphic hardware. The …

Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representation. CVPR, 2022.

Spiking Neural Network (SNN) is a promising energy-efficient AI model when implemented on neuromorphic hardware. However, it is a …

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 …

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 …

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 …


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