Search Results for author: Haonan Wang

Found 31 papers, 16 papers with code

Efficient Dataset Distillation via Minimax Diffusion

1 code implementation27 Nov 2023 Jianyang Gu, Saeed Vahidian, Vyacheslav Kungurtsev, Haonan Wang, Wei Jiang, Yang You, Yiran Chen

Observing that key factors for constructing an effective surrogate dataset are representativeness and diversity, we design additional minimax criteria in the generative training to enhance these facets for the generated images of diffusion models.

Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation

1 code implementation NeurIPS 2023 Haonan Wang, Xiaomeng Li

As a result, there is growing interest in using semi-supervised learning (SSL) techniques to train models with limited labeled data.

Domain Generalization Image Segmentation +4

Model-enhanced Vector Index

1 code implementation NeurIPS 2023 Hailin Zhang, Yujing Wang, Qi Chen, Ruiheng Chang, Ting Zhang, Ziming Miao, Yingyan Hou, Yang Ding, Xupeng Miao, Haonan Wang, Bochen Pang, Yuefeng Zhan, Hao Sun, Weiwei Deng, Qi Zhang, Fan Yang, Xing Xie, Mao Yang, Bin Cui

We empirically show that our model achieves better performance on the commonly used academic benchmarks MSMARCO Passage and Natural Questions, with comparable serving latency to dense retrieval solutions.

Natural Questions Quantization +1

Investigating Copyright Issues of Diffusion Models under Practical Scenarios

no code implementations15 Sep 2023 Yang Zhang, Teoh Tze Tzun, Lim Wei Hern, Haonan Wang, Kenji Kawaguchi

In our work, we tackle the limitations of previous studies by delving into partial copyright infringement, which treats parts of images as copyrighted content, using prompts that are considerably different from copyrighted topics.

Lightweight Super-Resolution Head for Human Pose Estimation

1 code implementation31 Jul 2023 Haonan Wang, Jie Liu, Jie Tang, Gangshan Wu

We first propose the SR head, which predicts heatmaps with a spatial resolution higher than the input feature maps (or even consistent with the input image) by super-resolution, to effectively reduce the quantization error and the dependence on further post-processing.

Pose Estimation Quantization +1

DHC: Dual-debiased Heterogeneous Co-training Framework for Class-imbalanced Semi-supervised Medical Image Segmentation

1 code implementation22 Jul 2023 Haonan Wang, Xiaomeng Li

Aiming to solve this issue, we present a novel Dual-debiased Heterogeneous Co-training (DHC) framework for semi-supervised 3D medical image segmentation.

Image Segmentation Semantic Segmentation +1

Subclass-balancing Contrastive Learning for Long-tailed Recognition

1 code implementation ICCV 2023 Chengkai Hou, Jieyu Zhang, Haonan Wang, Tianyi Zhou

We overcome these drawbacks by a novel ``subclass-balancing contrastive learning (SBCL)'' approach that clusters each head class into multiple subclasses of similar sizes as the tail classes and enforce representations to capture the two-layer class hierarchy between the original classes and their subclasses.

Contrastive Learning Representation Learning

Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective

no code implementations24 Jun 2023 Wei Huang, Yuan Cao, Haonan Wang, Xin Cao, Taiji Suzuki

Graph neural networks (GNNs) have pioneered advancements in graph representation learning, exhibiting superior feature learning and performance over multilayer perceptrons (MLPs) when handling graph inputs.

Graph Representation Learning Learning Theory +1

Dynamic Data Augmentation via MCTS for Prostate MRI Segmentation

1 code implementation25 May 2023 Xinyue Xu, Yuhan Hsi, Haonan Wang, Xiaomeng Li

However, manually configuring a generic augmentation combination and parameters for different datasets is non-trivial due to inconsistent acquisition approaches and data distributions.

Data Augmentation MRI segmentation

Boosting Visual-Language Models by Exploiting Hard Samples

no code implementations9 May 2023 Haonan Wang, Minbin Huang, Runhui Huang, Lanqing Hong, Hang Xu, Tianyang Hu, Xiaodan Liang, Zhenguo Li

On a comprehensive zero-shot and retrieval benchmark, without training the model from scratch or utilizing additional data, HELIP consistently boosts existing models to achieve leading performance.

Retrieval Zero-Shot Learning

Single-Pass Contrastive Learning Can Work for Both Homophilic and Heterophilic Graph

1 code implementation20 Nov 2022 Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang, Kenji Kawaguchi, Xiaokui Xiao

To answer this question, we theoretically study the concentration property of features obtained by neighborhood aggregation on homophilic and heterophilic graphs, introduce the single-pass augmentation-free graph contrastive learning loss based on the property, and provide performance guarantees for the minimizer of the loss on downstream tasks.

Contrastive Learning

BEBERT: Efficient and Robust Binary Ensemble BERT

1 code implementation28 Oct 2022 Jiayi Tian, Chao Fang, Haonan Wang, Zhongfeng Wang

Pre-trained BERT models have achieved impressive accuracy on natural language processing (NLP) tasks.

Binarization Knowledge Distillation

A Neural Corpus Indexer for Document Retrieval

1 code implementation6 Jun 2022 Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Hao Sun, Qi Chen, Yuqing Xia, Chengmin Chi, Guoshuai Zhao, Zheng Liu, Xing Xie, Hao Allen Sun, Weiwei Deng, Qi Zhang, Mao Yang

To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query.

Retrieval TriviaQA

Understanding Programmatic Weak Supervision via Source-aware Influence Function

no code implementations25 May 2022 Jieyu Zhang, Haonan Wang, Cheng-Yu Hsieh, Alexander Ratner

Programmatic Weak Supervision (PWS) aggregates the source votes of multiple weak supervision sources into probabilistic training labels, which are in turn used to train an end model.

Augmentation-Free Graph Contrastive Learning with Performance Guarantee

no code implementations11 Apr 2022 Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang

Graph contrastive learning (GCL) is the most representative and prevalent self-supervised learning approach for graph-structured data.

Contrastive Learning Self-Supervised Learning

Training Fair Deep Neural Networks by Balancing Influence

no code implementations15 Jan 2022 Haonan Wang, Ziwei Wu, Jingrui He

Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their practical application.


From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems

no code implementations28 Oct 2021 Yao Zhou, Haonan Wang, Jingrui He, Haixun Wang

With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications.

counterfactual Explainable Models +2

Deep Active Learning by Leveraging Training Dynamics

no code implementations16 Oct 2021 Haonan Wang, Wei Huang, Ziwei Wu, Andrew Margenot, Hanghang Tong, Jingrui He

Active learning theories and methods have been extensively studied in classical statistical learning settings.

Active Learning

UCTransNet: Rethinking the Skip Connections in U-Net from a Channel-wise Perspective with Transformer

3 code implementations9 Sep 2021 Haonan Wang, Peng Cao, Jiaqi Wang, Osmar R. Zaiane

Specifically, the CTrans module is an alternate of the U-Net skip connections, which consists of a sub-module to conduct the multi-scale Channel Cross fusion with Transformer (named CCT) and a sub-module Channel-wise Cross-Attention (named CCA) to guide the fused multi-scale channel-wise information to effectively connect to the decoder features for eliminating the ambiguity.

Ranked #2 on Medical Image Segmentation on GlaS (IoU metric)

Image Segmentation Medical Image Segmentation +2

Controllable Gradient Item Retrieval

1 code implementation31 May 2021 Haonan Wang, Chang Zhou, Carl Yang, Hongxia Yang, Jingrui He

A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence.

Disentanglement Retrieval

Adversarial Data Generation of Multi-category Marked Temporal Point Processes with Sparse, Incomplete, and Small Training Samples

no code implementations1 Jan 2021 Shashika Ranga Muramudalige, Anura Jayasumana, Haonan Wang

The transformation of training data to the distribution facilitates the accurate capture of underlying process characteristics despite the sparseness and incompleteness of data.

Point Processes

Secure Network Release with Link Privacy

no code implementations28 Sep 2020 Carl Yang, Haonan Wang, Ke Zhang, Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).

Graph Generation

Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization

1 code implementation NeurIPS 2021 Qi Zhu, Carl Yang, Yidan Xu, Haonan Wang, Chao Zhang, Jiawei Han

Graph neural networks (GNNs) have achieved superior performance in various applications, but training dedicated GNNs can be costly for large-scale graphs.

Knowledge Graphs Transfer Learning

Secure Deep Graph Generation with Link Differential Privacy

1 code implementation1 May 2020 Carl Yang, Haonan Wang, Ke Zhang, Liang Chen, Lichao Sun

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes).

Graph Generation Link Prediction

Relation Learning on Social Networks with Multi-Modal Graph Edge Variational Autoencoders

no code implementations4 Nov 2019 Carl Yang, Jieyu Zhang, Haonan Wang, Sha Li, Myungwan Kim, Matt Walker, Yiou Xiao, Jiawei Han

While node semantics have been extensively explored in social networks, little research attention has been paid to profile edge semantics, i. e., social relations.

Learning to Transfer via Modelling Multi-level Task Dependency

no code implementations25 Sep 2019 Haonan Wang, Zhenbang Wu, Ziniu Hu, Yizhou Sun

Besides, the understanding of relationships among tasks has been ignored by most of the current methods.

Multi-Task Learning

Design Light-weight 3D Convolutional Networks for Video Recognition Temporal Residual, Fully Separable Block, and Fast Algorithm

no code implementations31 May 2019 Haonan Wang, Jun Lin, Zhongfeng Wang

Deep 3-dimensional (3D) Convolutional Network (ConvNet) has shown promising performance on video recognition tasks because of its powerful spatio-temporal information fusion ability.

Video Recognition

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