Search Results for author: Haoliang Sun

Found 12 papers, 7 papers with code

Improving Generalization in Meta-Learning via Meta-Gradient Augmentation

1 code implementation14 Jun 2023 Ren Wang, Haoliang Sun, Qi Wei, Xiushan Nie, Yuling Ma, Yilong Yin

The key idea is to first break the rote memories by network pruning to address memorization overfitting in the inner loop, and then the gradients of pruned sub-networks naturally form the high-quality augmentation of the meta-gradient to alleviate learner overfitting in the outer loop.

Few-Shot Learning Memorization +1

MetaViewer: Towards A Unified Multi-View Representation

no code implementations CVPR 2023 Ren Wang, Haoliang Sun, Yuling Ma, Xiaoming Xi, Yilong Yin

To overcome them, we propose a novel bi-level-optimization-based multi-view learning framework, where the representation is learned in a uniform-to-specific manner.

MULTI-VIEW LEARNING Representation Learning

Fine-Grained Classification with Noisy Labels

no code implementations CVPR 2023 Qi Wei, Lei Feng, Haoliang Sun, Ren Wang, Chenhui Guo, Yilong Yin

To this end, we propose a novel framework called stochastic noise-tolerated supervised contrastive learning (SNSCL) that confronts label noise by encouraging distinguishable representation.

Classification Contrastive Learning +1

Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization

1 code implementation24 Aug 2022 Qi Wei, Haoliang Sun, Xiankai Lu, Yilong Yin

Sample selection is an effective strategy to mitigate the effect of label noise in robust learning.

Learning with noisy labels

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 +2

Attentional Prototype Inference for Few-Shot Segmentation

1 code implementation14 May 2021 Haoliang Sun, Xiankai Lu, Haochen Wang, Yilong Yin, XianTong Zhen, Cees G. M. Snoek, Ling Shao

We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution.

Bayesian Inference Few-Shot Semantic Segmentation +2

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 Few-Shot Learning +1

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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