Search Results for author: Pengfei Chen

Found 25 papers, 16 papers with code

Compress Polyphone Pronunciation Prediction Model with Shared Labels

no code implementations CCL 2020 Pengfei Chen, Lina Wang, Hui Di, Kazushige Ouchi, Lvhong Wang

In contrast to existing quantization with low precision data format and projection layer, we propose a novel method based on shared labels, which focuses on compressing the fully-connected layer before Softmax for models with a huge number of labels in TTS polyphone selection.


CPR++: Object Localization via Single Coarse Point Supervision

2 code implementations30 Jan 2024 Xuehui Yu, Pengfei Chen, Kuiran Wang, Xumeng Han, Guorong Li, Zhenjun Han, Qixiang Ye, Jianbin Jiao

CPR reduces the semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point.

Object Object Localization

Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes

1 code implementation ICCV 2023 Di wu, Pengfei Chen, Xuehui Yu, Guorong Li, Zhenjun Han, Jianbin Jiao

Object detection via inaccurate bounding boxes supervision has boosted a broad interest due to the expensive high-quality annotation data or the occasional inevitability of low annotation quality (\eg tiny objects).

Multiple Instance Learning Object +2

HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic Services

1 code implementation20 Apr 2023 Hui Dou, Shanshan Zhu, Yiwen Zhang, Pengfei Chen, Zibin Zheng

Besides, experiments with different training datasets, different optimization objectives and different machine learning platforms verify that HyperTuner can well adapt to various data analytic service scenarios.

Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training

no code implementations10 May 2022 Cheng Xue, Lequan Yu, Pengfei Chen, Qi Dou, Pheng-Ann Heng

In this paper, we propose a novel collaborative training paradigm with global and local representation learning for robust medical image classification from noisy-labeled data to combat the lack of high quality annotated medical data.

Image Classification Medical Image Classification +1

Acknowledging the Unknown for Multi-label Learning with Single Positive Labels

1 code implementation30 Mar 2022 Donghao Zhou, Pengfei Chen, Qiong Wang, Guangyong Chen, Pheng-Ann Heng

Due to the difficulty of collecting exhaustive multi-label annotations, multi-label datasets often contain partial labels.

Multi-Label Learning Weakly-supervised Learning

Object Localization under Single Coarse Point Supervision

2 code implementations CVPR 2022 Xuehui Yu, Pengfei Chen, Di wu, Najmul Hassan, Guorong Li, Junchi Yan, Humphrey Shi, Qixiang Ye, Zhenjun Han

In this study, we propose a POL method using coarse point annotations, relaxing the supervision signals from accurate key points to freely spotted points.

Multiple Instance Learning Object +1

Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment

1 code implementation ICCV 2021 Pengfei Chen, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi

From this adaptation, we split the data in target domain into confident and uncertain subdomains using the proposed uncertainty-based ranking function, through measuring their prediction confidences.

Unsupervised Domain Adaptation Video Quality Assessment +1

Noise against noise: stochastic label noise helps combat inherent label noise

no code implementations ICLR 2021 Pengfei Chen, Guangyong Chen, Junjie Ye, Jingwei Zhao, Pheng-Ann Heng

The noise in stochastic gradient descent (SGD) provides a crucial implicit regularization effect, previously studied in optimization by analyzing the dynamics of parameter updates.

Learning with noisy labels

Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise

1 code implementation10 Dec 2020 Pengfei Chen, Junjie Ye, Guangyong Chen, Jingwei Zhao, Pheng-Ann Heng

In this work, we present a theoretical hypothesis testing and prove that noise in real-world dataset is unlikely to be CCN, which confirms that label noise should depend on the instance and justifies the urgent need to go beyond the CCN assumption. The theoretical results motivate us to study the more general and practical-relevant instance-dependent noise (IDN).

Image Classification

Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

1 code implementation8 Dec 2020 Pengfei Chen, Junjie Ye, Guangyong Chen, Jingwei Zhao, Pheng-Ann Heng

For validation, we prove that a noisy validation set is reliable, addressing the critical demand of model selection in scenarios like hyperparameter-tuning and early stopping.

Learning with noisy labels Model Selection +1

Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network

no code implementations5 Aug 2020 Yihang Zhang, Aristotelis-Angelos Papadopoulos, Pengfei Chen, Faisal Alasiri, Tianchen Yuan, Jin Zhou, Petros A. Ioannou

In this paper, we design an integrated simulation-prediction system which estimates the Origin-Destination (OD) matrix of a road network using only flow rate information and predicts the behavior of the road network in different simulation scenarios.

Decision Making Management

A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR

no code implementations13 Jun 2019 Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang

Most recent efforts have been devoted to defending noisy labels by discarding noisy samples from the training set or assigning weights to training samples, where the weight associated with a noisy sample is expected to be small.

Data Augmentation

Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization

no code implementations13 Jun 2019 Pengfei Chen, Weiwen Liu, Chang-Yu Hsieh, Guangyong Chen, Shengyu Zhang

The IGNN model is based on an elegant and fundamental idea in information theory as explained in the main text, and it could be easily generalized beyond the contexts of molecular graphs considered in this work.

Drug Discovery Quantum Chemistry Regression

Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks

1 code implementation15 May 2019 Guangyong Chen, Pengfei Chen, Yujun Shi, Chang-Yu Hsieh, Benben Liao, Shengyu Zhang

Our work is based on an excellent idea that whitening the inputs of neural networks can achieve a fast convergence speed.

Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels

3 code implementations13 May 2019 Pengfei Chen, Benben Liao, Guangyong Chen, Shengyu Zhang

Noisy labels are ubiquitous in real-world datasets, which poses a challenge for robustly training deep neural networks (DNNs) as DNNs usually have the high capacity to memorize the noisy labels.

Image Classification Test

Log Hyperbolic Cosine Loss Improves Variational Auto-Encoder

no code implementations27 Sep 2018 Pengfei Chen, Guangyong Chen, Shengyu Zhang

In Variational Auto-Encoder (VAE), the default choice of reconstruction loss function between the decoded sample and the input is the squared $L_2$.


Understanding Convolution for Semantic Segmentation

5 code implementations27 Feb 2017 Panqu Wang, Pengfei Chen, Ye Yuan, Ding Liu, Zehua Huang, Xiaodi Hou, Garrison Cottrell

This framework 1) effectively enlarges the receptive fields (RF) of the network to aggregate global information; 2) alleviates what we call the "gridding issue" caused by the standard dilated convolution operation.

Segmentation Semantic Segmentation +2

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