140 citations · 820 across the 54 of their papers we have counts for
8 papers · 1 filter
Offset Curves Loss for Imbalanced Problem in Medical Segmentation
Ngan Le, Trung Le, Kashu Yamazaki +3
Medical image segmentation has played an important role in medical analysis and widely developed for many clinical applications. Deep learning-based approaches have achieved high p…
A Multi-task Contextual Atrous Residual Network for Brain Tumor Detection & Segmentation
Ngan Le, Kashu Yamazaki, Dat Truong +2
In recent years, deep neural networks have achieved state-of-the-art performance in a variety of recognition and segmentation tasks in medical imaging including brain tumor segment…
Online Ensemble Model Compression using Knowledge Distillation
Devesh Walawalkar, Zhiqiang Shen, Marios Savvides
This paper presents a novel knowledge distillation based model compression framework consisting of a student ensemble. It enables distillation of simultaneously learnt ensemble kno…
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks
Zhiqiang Shen, Marios Savvides
We introduce a simple yet effective distillation framework that is able to boost the vanilla ResNet-50 to 80%+ Top-1 accuracy on ImageNet without tricks. We construct such a framew…
Binarizing MobileNet via Evolution-based Searching
Hai Phan, Zechun Liu, Dang Huynh +3
Binary Neural Networks (BNNs), known to be one among the effectively compact network architectures, have achieved great outcomes in the visual tasks. Designing efficient binary arc…
Un-Mix: Rethinking Image Mixtures for Unsupervised Visual Representation Learning
Zhiqiang Shen, Zechun Liu, Zhuang Liu +3
The recently advanced unsupervised learning approaches use the siamese-like framework to compare two "views" from the same image for learning representations. Making the two views…
ReActNet: Towards Precise Binary Neural Network with Generalized Activation Functions
Zechun Liu, Zhiqiang Shen, Marios Savvides +1
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. We f…
Solving Missing-Annotation Object Detection with Background Recalibration Loss
Han Zhang, Fangyi Chen, Zhiqiang Shen +3
This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets, so these missing-labeled areas will be regarded…