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07/02/2023
Defending Against Malicious Behaviors in Federated Learning with Blockchain
In the era of deep learning, federated learning (FL) presents a promisin...
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11/16/2022
Region Proposal Network Pre-Training Helps Label-Efficient Object Detection
Self-supervised pre-training, based on the pretext task of instance disc...
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11/05/2022
FLock: Defending Malicious Behaviors in Federated Learning with Blockchain
Federated learning (FL) is a promising way to allow multiple data owners...
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06/30/2022
Learning Underrepresented Classes from Decentralized Partially Labeled Medical Images
Using decentralized data for federated training is one promising emergin...
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04/19/2022
Revisiting Vicinal Risk Minimization for Partially Supervised Multi-Label Classification Under Data Scarcity
Due to the high human cost of annotation, it is non-trivial to curate a ...
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10/06/2021
Cooperative Multi-Agent Actor-Critic for Privacy-Preserving Load Scheduling in a Residential Microgrid
As a scalable data-driven approach, multi-agent reinforcement learning (...
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09/15/2021
Federated Contrastive Learning for Decentralized Unlabeled Medical Images
A label-efficient paradigm in computer vision is based on self-supervise...
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06/18/2021
Residual Contrastive Learning for Joint Demosaicking and Denoising
The breakthrough of contrastive learning (CL) has fueled the recent succ...
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05/20/2021
Negational Symmetry of Quantum Neural Networks for Binary Pattern Classification
Entanglement is a physical phenomenon, which has fueled recent successes...
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11/28/2020
Towards Robust Medical Image Segmentation on Small-Scale Data with Incomplete Labels
The data-driven nature of deep learning models for semantic segmentation...
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05/28/2019
Adversarial Domain Adaptation Being Aware of Class Relationships
Adversarial training is a useful approach to promote the learning of tra...
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10/08/2018
Toward Understanding the Impact of Staleness in Distributed Machine Learning
Many distributed machine learning (ML) systems adopt the non-synchronous...
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07/29/2018
Reinforced Auto-Zoom Net: Towards Accurate and Fast Breast Cancer Segmentation in Whole-slide Images
Convolutional neural networks have led to significant breakthroughs in t...
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07/10/2018
Unsupervised Domain Adaptation for Automatic Estimation of Cardiothoracic Ratio
The cardiothoracic ratio (CTR), a clinical metric of heart size in chest...
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04/20/2018
ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation
Salient segmentation aims to segment out attention-grabbing regions, a c...
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03/26/2017