Our analysis of caption models with SC loss shows that the performance degradation is caused by the increasingly noisy estimation of reward and baseline with fewer language resources.
To this end, based on a given CNN model, we first generate a CNN architecture space in which each architecture is a multi-stage CNN generated from the given model using some predefined transformations.
Aspect category detection (ACD) in sentiment analysis aims to identify the aspect categories mentioned in a sentence.
In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions.
Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN).
Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.
The attention is expected to concentrate on opinion words for accurate sentiment prediction.
Cross-domain sentiment classification has drawn much attention in recent years.
In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.
Improving the captioning performance on low-resource languages by leveraging English caption datasets has received increasing research interest in recent years.
(2) privacy leakage: the model trained using a conventional method may involuntarily reveal the private information of the patients in the training dataset.
Aspect level sentiment classification is a fine-grained sentiment analysis task.
In healthcare, applying deep learning models to electronic health records (EHRs) has drawn considerable attention.
This paper considers WCE-based gastric ulcer detection, in which the major challenge is to detect the lesions in a local region.
Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has).