Search Results for author: Lanqing Hong

Found 20 papers, 8 papers with code

Generalizing Few-Shot NAS with Gradient Matching

1 code implementation ICLR 2022 Shoukang Hu, Ruochen Wang, Lanqing Hong, Zhenguo Li, Cho-Jui Hsieh, Jiashi Feng

Efficient performance estimation of architectures drawn from large search spaces is essential to Neural Architecture Search.

Neural Architecture Search

CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving

no code implementations15 Mar 2022 Kaican Li, Kai Chen, Haoyu Wang, Lanqing Hong, Chaoqiang Ye, Jianhua Han, Yukuai Chen, Wei zhang, Chunjing Xu, Dit-yan Yeung, Xiaodan Liang, Zhenguo Li, Hang Xu

One main reason that impedes the development of truly reliably self-driving systems is the lack of public datasets for evaluating the performance of object detectors on corner cases.

Autonomous Driving Object Detection

Memory Replay with Data Compression for Continual Learning

1 code implementation ICLR 2022 Liyuan Wang, Xingxing Zhang, Kuo Yang, Longhui Yu, Chongxuan Li, Lanqing Hong, Shifeng Zhang, Zhenguo Li, Yi Zhong, Jun Zhu

In this work, we propose memory replay with data compression (MRDC) to reduce the storage cost of old training samples and thus increase their amount that can be stored in the memory buffer.

Autonomous Driving class-incremental learning +4

How Well Does Self-Supervised Pre-Training Perform with Streaming ImageNet?

no code implementations NeurIPS Workshop ImageNet_PPF 2021 Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, Jiashi Feng

Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.

Self-Supervised Learning

NAS-OoD: Neural Architecture Search for Out-of-Distribution Generalization

no code implementations ICCV 2021 Haoyue Bai, Fengwei Zhou, Lanqing Hong, Nanyang Ye, S. -H. Gary Chan, Zhenguo Li

In this work, we propose robust Neural Architecture Search for OoD generalization (NAS-OoD), which optimizes the architecture with respect to its performance on generated OoD data by gradient descent.

Domain Generalization Neural Architecture Search +1

Adversarial Robustness for Unsupervised Domain Adaptation

no code implementations ICCV 2021 Muhammad Awais, Fengwei Zhou, Hang Xu, Lanqing Hong, Ping Luo, Sung-Ho Bae, Zhenguo Li

Extensive Unsupervised Domain Adaptation (UDA) studies have shown great success in practice by learning transferable representations across a labeled source domain and an unlabeled target domain with deep models.

Adversarial Robustness Unsupervised Domain Adaptation

MultiSiam: Self-supervised Multi-instance Siamese Representation Learning for Autonomous Driving

1 code implementation ICCV 2021 Kai Chen, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-yan Yeung

By pre-training on SODA10M, a large-scale autonomous driving dataset, MultiSiam exceeds the ImageNet pre-trained MoCo-v2, demonstrating the potential of domain-specific pre-training.

Autonomous Driving Image Clustering +2

Test-Agnostic Long-Tailed Recognition by Test-Time Aggregating Diverse Experts with Self-Supervision

1 code implementation20 Jul 2021 Yifan Zhang, Bryan Hooi, Lanqing Hong, Jiashi Feng

To handle this task, we propose a new method, called Test-time Aggregating Diverse Experts, that presents two solution strategies: (1) a new skill-diverse expert learning strategy that trains diverse experts to excel at handling different class distributions from a single long-tailed training distribution; (2) a novel test-time expert aggregation strategy that leverages self-supervision to aggregate multiple experts for handling various unknown test distributions.

Image Classification Long-tail Learning

SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving

no code implementations21 Jun 2021 Jianhua Han, Xiwen Liang, Hang Xu, Kai Chen, Lanqing Hong, Jiageng Mao, Chaoqiang Ye, Wei zhang, Zhenguo Li, Xiaodan Liang, Chunjing Xu

Experiments show that SODA10M can serve as a promising pre-training dataset for different self-supervised learning methods, which gives superior performance when fine-tuning with different downstream tasks (i. e., detection, semantic/instance segmentation) in autonomous driving domain.

Autonomous Driving Instance Segmentation +4

Contextualizing Multiple Tasks via Learning to Decompose

no code implementations15 Jun 2021 Han-Jia Ye, Da-Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, De-Chuan Zhan

One single instance could possess multiple portraits and reveal diverse relationships with others according to different contexts.

Few-Shot Image Classification Meta-Learning

How Well Does Self-Supervised Pre-Training Perform with Streaming Data?

no code implementations ICLR 2022 Dapeng Hu, Shipeng Yan, Qizhengqiu Lu, Lanqing Hong, Hailin Hu, Yifan Zhang, Zhenguo Li, Xinchao Wang, Jiashi Feng

Prior works on self-supervised pre-training focus on the joint training scenario, where massive unlabeled data are assumed to be given as input all at once, and only then is a learner trained.

Representation Learning Self-Supervised Learning

Relaxed Conditional Image Transfer for Semi-supervised Domain Adaptation

no code implementations5 Jan 2021 Qijun Luo, Zhili Liu, Lanqing Hong, Chongxuan Li, Kuo Yang, Liyuan Wang, Fengwei Zhou, Guilin Li, Zhenguo Li, Jun Zhu

Semi-supervised domain adaptation (SSDA), which aims to learn models in a partially labeled target domain with the assistance of the fully labeled source domain, attracts increasing attention in recent years.

Domain Adaptation

DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning

no code implementations1 Jan 2021 Kaichen Zhou, Lanqing Hong, Fengwei Zhou, Binxin Ru, Zhenguo Li, Trigoni Niki, Jiashi Feng

Our method performs co-optimization of the neural architectures, training hyper-parameters and data augmentation policies in an end-to-end fashion without the need of model retraining.

Data Augmentation Neural Architecture Search

Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models

1 code implementation NeurIPS Workshop ICBINB 2020 Fan Bao, Kun Xu, Chongxuan Li, Lanqing Hong, Jun Zhu, Bo Zhang

The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition functions in such models are generally intractable.

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