对抗训练用于确保共享空间只包含共有的信息,正交约束用来消除共享和特有空间冗余的特征。 Adversarial Multi-task Learning 黄色lstm用于提取共有特征,灰色提取任务的私有特征。 任务k的公有特征 skt=LSTM(xt,skt−1,θs) 任务k私有的特征 hkt=LSTM(xt,hmt−1,θk) 问题1:如何保证共享编码器提取的是公有特征...
This study proposes a writer adaptation technique for Arabic online handwriting recognition systems that employs adversarial Multi-Task Learning (MTL). Adversarial training and MTL modify the deep-features distribution of the Writer Dependent (WD) model, leading its output to closely resemble that of ...
最近看了复旦大学邱锡鹏老师组于2017年的文章《Adversarial Multi-task Learning for Text Classification》,这篇论文的观点比较明确,从中可以学习很多nlp思维。 1.论文摘要指出:神经网络模型因为学习共享层用于捕获任务共有的和与任务相关的特征而在mutil-task任务中表现出很不错的性能。但是现有大多数方法在提取特征时,...
创新点: Adversarial learning + Orthogonality Constraints Abstract 背景:神经网络模型已经表明了它们在Multi-task Learning中的广阔前景,其重点是学习共享层以提取公共的和随不同任务不变的特征。 存在的问题:但在大多数现有方法中,提取的 shared features易受到特定于任务的功能或其他任务带来的噪音的污染。 本文的方...
An adversarial multi-task learning scheme for speaker-invariant training can be implemented, aiming at actively curtailing the inter-talker feature variability, while maximizing its senone discriminability to enhance the performance of a deep neural network (DNN) based automatic speech recognition system....
• We propose an adversarial learning method to capture multi-task dependencies from both representation-level and label-level.• We design a recognizer and a discriminator for multi-task face analyses.• Experimental results on benchmark datasets demonstrate the superiority of the proposed method...
MMSRNet: Pathological image super-resolution by multi-task and multi-scale learning 2023, Biomedical Signal Processing and Control Citation Excerpt : The number of RG block is empirically set to 10 while each RG contains 20 RCABs. Multi-task deep learning is a popular modeling paradigm [44,45...
samples. Notably, the aim of this approach is to train a data loading quantum channel for generic probability distributions. As discussed before, GAN-based learning is explicitly suitable for capturing not only uni-modal but also multi-modal distributions, as we will demonstrate later in this ...
The loss of this multi-task learning framework can be decomposed into thesupervised loss , and theGAN lossof a discriminator , During the training phase, we jointly minimize the total loss obtained by simply combining the two losses together. ...
Robust multi-task learning with t -processes Most current multi-task learning frameworks ignore the robustness issue, which means that the presence of "outlier" tasks may greatly reduce overall system performance. We introduce a robust framework for Bayesian multitask learning, t-p... S Yu,V ...