Search Results for author: Haoliang Sun

Found 8 papers, 4 papers with code

Learning to Rectify for Robust Learning with Noisy Labels

1 code implementation8 Nov 2021 Haoliang Sun, Chenhui Guo, Qi Wei, Zhongyi Han, Yilong Yin

In this paper, we propose warped probabilistic inference (WarPI) to achieve adaptively rectifying the training procedure for the classification network within the meta-learning scenario.

Learning with noisy labels Meta-Learning

Learning Transferable Parameters for Unsupervised Domain Adaptation

1 code implementation13 Aug 2021 Zhongyi Han, Haoliang Sun, Yilong Yin

However, the learning processes of domain-invariant features and source hypothesis inevitably involve domain-specific information that would degrade the generalizability of UDA models on the target domain.

Image Classification Keypoint Detection +1

Attentional Prototype Inference for Few-Shot Semantic Segmentation

no code implementations14 May 2021 Haoliang Sun, Xiankai Lu, Haochen Wang, Yilong Yin, XianTong Zhen, Cees G. M. Snoek, Ling Shao

In this work, we propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot semantic segmentation.

Bayesian Inference Few-Shot Semantic Segmentation +1

MetaKernel: Learning Variational Random Features with Limited Labels

no code implementations8 May 2021 Yingjun Du, Haoliang Sun, XianTong Zhen, Jun Xu, Yilong Yin, Ling Shao, Cees G. M. Snoek

Specifically, we propose learning variational random features in a data-driven manner to obtain task-specific kernels by leveraging the shared knowledge provided by related tasks in a meta-learning setting.

Few-Shot Image Classification Variational Inference

Direct Estimation of Spinal Cobb Angles by Structured Multi-Output Regression

no code implementations23 Dec 2020 Haoliang Sun, XianTong Zhen, Chris Bailey, Parham Rasoulinejad, Yilong Yin, Shuo Li

The Cobb angle that quantitatively evaluates the spinal curvature plays an important role in the scoliosis diagnosis and treatment.

Learning to Learn Kernels with Variational Random Features

1 code implementation ICML 2020 Xiantong Zhen, Haoliang Sun, Ying-Jun Du, Jun Xu, Yilong Yin, Ling Shao, Cees Snoek

We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable.

Few-Shot Learning Variational Inference

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