Search Results for author: Kwang-Ting Cheng

Found 26 papers, 13 papers with code

FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

1 code implementation4 May 2022 Jeffry Wicaksana, Zengqiang Yan, Dong Zhang, Xijie Huang, Huimin Wu, Xin Yang, Kwang-Ting Cheng

To relax this assumption, in this work, we propose a label-agnostic unified federated learning framework, named FedMix, for medical image segmentation based on mixed image labels.

Federated Learning Lesion Segmentation +2

Stereo Neural Vernier Caliper

1 code implementation21 Mar 2022 Shichao Li, Zechun Liu, Zhiqiang Shen, Kwang-Ting Cheng

We propose a new object-centric framework for learning-based stereo 3D object detection.

3D Object Detection

Label Propagation for Annotation-Efficient Nuclei Segmentation from Pathology Images

no code implementations16 Feb 2022 Yi Lin, Zhiyong Qu, Hao Chen, Zhongke Gao, Yuexiang Li, Lili Xia, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng

Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H\&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm.

Representation Learning

Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space

1 code implementation3 Jan 2022 Arnav Chavan, Zhiqiang Shen, Zhuang Liu, Zechun Liu, Kwang-Ting Cheng, Eric Xing

This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework.

Nonuniform-to-Uniform Quantization: Towards Accurate Quantization via Generalized Straight-Through Estimation

1 code implementation29 Nov 2021 Zechun Liu, Kwang-Ting Cheng, Dong Huang, Eric Xing, Zhiqiang Shen

The nonuniform quantization strategy for compressing neural networks usually achieves better performance than its counterpart, i. e., uniform strategy, due to its superior representational capacity.


Joint stereo 3D object detection and implicit surface reconstruction

no code implementations25 Nov 2021 Shichao Li, Kwang-Ting Cheng

We then propose a new instance-level network that addresses the unseen surface hallucination problem by extracting point-based representations from stereo region-of-interests, and infers implicit shape codes with predicted complete surface geometry.

3D Object Detection Surface Reconstruction

Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

no code implementations22 Nov 2021 Huimin Wu, Xiaomeng Li, Kwang-Ting Cheng

To enhance the representation learning, we propose a stage-adaptive contrastive learning method, including a boundary-aware contrastive loss to regularize the labeled images in the first stage and a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage.

Contrastive Learning Representation Learning +2

Automated Pulmonary Embolism Detection from CTPA Images Using an End-to-End Convolutional Neural Network

no code implementations10 Nov 2021 Yi Lin, Jianchao Su, Xiang Wang, Xiang Li, Jingen Liu, Kwang-Ting Cheng, Xin Yang

We have evaluated our approach using the 20 CTPA test dataset from the PE challenge, achieving a sensitivity of 78. 9%, 80. 7% and 80. 7% at 2 false positives per volume at 0mm, 2mm and 5mm localization error, which is superior to the state-of-the-art methods.

Pulmonary Embolism Detection

R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors

no code implementations7 Jul 2021 Dawen Xu, Meng He, Cheng Liu, Ying Wang, Long Cheng, Huawei Li, Xiaowei Li, Kwang-Ting Cheng

It takes the remote AIoT processor with soft errors in the training loop such that the on-site computing errors can be learned with the application data on the server and the retrained models can be resilient to the soft errors.

How Do Adam and Training Strategies Help BNNs Optimization?

no code implementations21 Jun 2021 Zechun Liu, Zhiqiang Shen, Shichao Li, Koen Helwegen, Dong Huang, Kwang-Ting Cheng

We show the regularization effect of second-order momentum in Adam is crucial to revitalize the weights that are dead due to the activation saturation in BNNs.

LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction

no code implementations12 Jun 2021 Yi Lin, Yanfei Liu, Hao Chen, Xin Yang, Kai Ma, Yefeng Zheng, Kwang-Ting Cheng

To the best of our knowledge, this is the first attempt to investigate NAS and knowledge distillation in ensemble learning, especially in the field of medical image analysis.

Ensemble Learning Knowledge Distillation +1

S2-BNN: Bridging the Gap Between Self-Supervised Real and 1-bit Neural Networks via Guided Distribution Calibration

1 code implementation CVPR 2021 Zhiqiang Shen, Zechun Liu, Jie Qin, Lei Huang, Kwang-Ting Cheng, Marios Savvides

In this paper, we focus on this more difficult scenario: learning networks where both weights and activations are binary, meanwhile, without any human annotated labels.

Contrastive Learning Self-Supervised Learning

Partial Is Better Than All: Revisiting Fine-tuning Strategy for Few-shot Learning

no code implementations8 Feb 2021 Zhiqiang Shen, Zechun Liu, Jie Qin, Marios Savvides, Kwang-Ting Cheng

A common practice for this task is to train a model on the base set first and then transfer to novel classes through fine-tuning (Here fine-tuning procedure is defined as transferring knowledge from base to novel data, i. e. learning to transfer in few-shot scenario.)

Few-Shot Learning

Cascaded deep monocular 3D human pose estimation with evolutionary training data

1 code implementation CVPR 2020 Shichao Li, Lei Ke, Kevin Pratama, Yu-Wing Tai, Chi-Keung Tang, Kwang-Ting Cheng

End-to-end deep representation learning has achieved remarkable accuracy for monocular 3D human pose estimation, yet these models may fail for unseen poses with limited and fixed training data.

Data Augmentation Monocular 3D Human Pose Estimation +2

Joint Multi-Dimension Pruning via Numerical Gradient Update

no code implementations18 May 2020 Zechun Liu, Xiangyu Zhang, Zhiqiang Shen, Zhe Li, Yichen Wei, Kwang-Ting Cheng, Jian Sun

To tackle these three naturally different dimensions, we proposed a general framework by defining pruning as seeking the best pruning vector (i. e., the numerical value of layer-wise channel number, spacial size, depth) and construct a unique mapping from the pruning vector to the pruned network structures.

Binarizing MobileNet via Evolution-based Searching

no code implementations CVPR 2020 Hai Phan, Zechun Liu, Dang Huynh, Marios Savvides, Kwang-Ting Cheng, Zhiqiang Shen

Inspired by one-shot architecture search frameworks, we manipulate the idea of group convolution to design efficient 1-Bit Convolutional Neural Networks (CNNs), assuming an approximately optimal trade-off between computational cost and model accuracy.

ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions

1 code implementation ECCV 2020 Zechun Liu, Zhiqiang Shen, Marios Savvides, Kwang-Ting Cheng

In this paper, we propose several ideas for enhancing a binary network to close its accuracy gap from real-valued networks without incurring any additional computational cost.

Facial age estimation by deep residual decision making

2 code implementations28 Aug 2019 Shichao Li, Kwang-Ting Cheng

Residual representation learning simplifies the optimization problem of learning complex functions and has been widely used by traditional convolutional neural networks.

Age Estimation Decision Making +1

Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization

3 code implementations NeurIPS 2019 Koen Helwegen, James Widdicombe, Lukas Geiger, Zechun Liu, Kwang-Ting Cheng, Roeland Nusselder

Together, the redefinition of latent weights as inertia and the introduction of Bop enable a better understanding of BNN optimization and open up the way for further improvements in training methodologies for BNNs.

Visualizing the decision-making process in deep neural decision forest

1 code implementation19 Apr 2019 Shichao Li, Kwang-Ting Cheng

Deep neural decision forest (NDF) achieved remarkable performance on various vision tasks via combining decision tree and deep representation learning.

Decision Making General Classification +1

Semi-supervised mp-MRI Data Synthesis with StitchLayer and Auxiliary Distance Maximization

no code implementations17 Dec 2018 Zhiwei Wang, Yi Lin, Kwang-Ting Cheng, Xin Yang

Experimental results show that our method can effectively synthesize a large variety of mpMRI images which contain meaningful CS PCa lesions, display a good visual quality and have the correct paired relationship.

Synthesizing Multi-Parameter Magnetic Resonance Imaging (Mp-Mri) Data

Bi-Real Net: Binarizing Deep Network Towards Real-Network Performance

1 code implementation4 Nov 2018 Zechun Liu, Wenhan Luo, Baoyuan Wu, Xin Yang, Wei Liu, Kwang-Ting Cheng

To address the training difficulty, we propose a training algorithm using a tighter approximation to the derivative of the sign function, a magnitude-aware gradient for weight updating, a better initialization method, and a two-step scheme for training a deep network.

Depth Estimation

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