Search Results for author:
Found 8 papers, 5 papers with code
Compete to Win: Enhancing Pseudo Labels for Barely-supervised Medical Image Segmentation
This study investigates barely-supervised medical image segmentation where only few labeled data, i. e., single-digit cases are available.
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning
Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part.
Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity
To solve this puzzle, in this paper, we focus on the $\ell_0$ constrained black-box stochastic optimization problems, and propose a new stochastic zeroth-order gradient hard-thresholding (SZOHT) algorithm with a general ZO gradient estimator powered by a novel random support sampling.
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
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.
Deep Understanding based Multi-Document Machine Reading Comprehension
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two kinds of understandings.
Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation
A stage-adaptive contrastive learning method is proposed, containing a boundary-aware contrastive loss that takes advantage of the labeled images in the first stage, as well as a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage.
Efficient Semi-Supervised Adversarial Training without Guessing Labels
To adapt to semi-supervised learning problems, they need to estimate labels for unlabeled data in advance, which inevitably degenerates the performance of the learned model due to the bias on the estimation of labels for unlabeled data.
Fast and Scalable Adversarial Training of Kernel SVM via Doubly Stochastic Gradients
Although a wide range of researches have been done in recent years to improve the adversarial robustness of learning models, but most of them are limited to deep neural networks (DNNs) and the work for kernel SVM is still vacant.
