论文题为《Semantic Autoencoder for Zero-Shot Learning》 论文主要提出了一种新的建立分类器算法:SAE(Semantic AutoEncoder),通过引入自编码器的结构较好解决了domain shift problem。 符号: X∈Rd∗N 代表d 维共N 个特征向量组成的矩阵,投影矩阵 W∈Rk∗d ,将特征向量投影到语义空间
Existing zero-shot learning (ZSL) models typically learn a projection function from a feature space to a semantic embedding space (e.g.~attribute space). However, such a projection function is only concerned with predicting the training seen class semantic representation (e.g.~attribute prediction...
内容提示: Semantic Autoencoder for Zero-Shot LearningElyor Kodirov Tao Xiang Shaogang GongQueen Mary University of London, UK{e.kodirov, t.xiang, s.gong}@qmul.ac.ukAbstractExisting zero-shot learning (ZSL) models typically learna projection function from a feature space to a semantic em-...
Both the encoder and decoder projection functions in our SAE model (SAE (W) and SAE (WT) respectively) can be used for effective ZSL. The encoder projection function seems to be slightly better overall. Measures how well a zero-shot learning method can trade-off between recognising data from...
In this work, we present a novel solution to ZSL based on learning a Semantic AutoEncoder (SAE). Taking the encoder-decoder paradigm, an encoder aims to project a visual feature vector into the semantic space as in the existing ZSL models. However, the decoder exerts an additional constraint...
Zero-shot learning via visual-semantic aligned autoencoder Zero-shot learning aims to learn a visual classifier for a category which has no training samples leveraging its semantic information and its relationship ... T Wei,J Huang,C Jin - 《Mathematical Biosciences & Engineering》 被引量: 0发表...
我们的代码可用于:\ URL {此HTTPS URL}。* Cluster-based Contrastive Disentangling for Generalized Zero-Shot Learning* 链接: arxiv.org/abs/2203.0264* 作者: Yi Gao,Chenwei Tang,Jiancheng Lv* 摘要: 广义零射击学习(GZSL)旨在通过仅训练所看到的课程来识别看到和看不见的课程,其中看不见的课程的情况往往...
Kodirov E, Xiang T, Gong S. Semantic autoencoder for zero-shot learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 3174-3183. 继续摆烂。。 思想简记 主要是建立了SAE(Semantic AutoEncoder),通过引入重构思想来解决domain shift problem。
Semantic Autoencoder for Zero-Shot learning本文使用自编码器来有监督地学习词向量地映射,效果良好,网络结构简单,但是在真实场景中,怎...
AI is introduced for building the SemCom to reconstruct and denoise the received semantic data frame at the receiver end. In particular, the Generative Adversarial Network (GAN) mechanism is designed to maintain a superior quality reconstruction under different signal-to-noise (SNR) channel ...