Deep Q Network NLP: Natural Language Processing CV: Computer Vision UDA: Unsupervised Domain Adaptation DANN: Domain Adversarial Neural Network CyCADA: Cycle-Consistent Adversarial Domain Adaptation SIFA: Synergistic Image and Feature Alignment HCA-DAN: Hierarchical Class-Aware Domain Adaptive Net...
Domain Adaptive Neural Networks for Object Recognition 作者第一次将Maximum Mean Discrepancy (MMD)和神经网络训练结合。文中神经网络结构很简单,只有一层隐藏层。主要想法是目标函数在原先的分类损失的基础上,将源域和目标域在隐藏层的表示差异基于MMD最小化。即: ...
TheMNIST-DANN.ipynbnotebook implements the MNIST experiments for the paper with the same model and optimization parameters, including the learning rate and adaptation parameter schedules. Rough results are below (more training would likely improve results - # epochs are not reported in the paper). ...
Domain Adaptive Neural Networks for Object Recognition 本篇是迁移学习专栏介绍的第五篇论文,PRICAI 2014的DaNN(Domain Adaptive Neural Network)。 Abstract 提出了一种简单的神经网络模型来处理目标识别中的Domain Adaptive问题。我们的模型将Maximum Mean Discrepancy(MMD)作为正则化方法引入监督学习中,以减少潜在空间...
distributions between the domains, thus obtaining a domain-invariant representation. Various methods have been proposed to achieve this, including the domain antagonisticneural network(DANN), which employs antagonistic training to align the source and target feature distributions at either a functional or...
an adaptive thresholding with a decision rule algorithm that allows to distinguish ECG signal peak from noise peak and also allows to discriminate T-waves. Once detected the NN interval, defined as the Normal-to-Normal interval obtained from the signal without abnormal beats, several features relate...
Their algorithm, named the domain adversarial neural network (DANN), contains a task predictor and an adversarial predictor. The task predictor was designed as an emotion classifier, while the adversarial predictor was designed as a subject classifier. These classifiers work in an adversarial manner ...
CVLAB-Unibo/Real-time-self-adaptive-deep-stereo Star420 Code Issues Pull requests Code for "Real-time self-adaptive deep stereo" - CVPR 2019 (ORAL) tensorflowstereo-visionpretrained-weightsunsupervised-machine-learningdomain-adaptationdispnetonline-adaptationmadnetcvpr2019cvpr2019-oraldeep-stereo-network ...
In the second step, we input the data reconstructed by the smart filter into a domain adversarial neural network (DANN). To learn domain-invariant and discriminative features, the learnable modules of SFDANN are trained in a unified manner with three objectives: time-frequency feature proximity, ...
Second, an alternative optimization method is derived to seek the optimal network parameters while pushing the hyperspheres built in the source domain and target domain to be as identical as possible. Through transferring one-class detection rule in the adaptive extraction of domain-invariant feature ...