Machine ID is employed to constrain the latent space of the Transformer-based autoencoder (TransAE) by introducing a simple ID classifier to learn the difference in the distribution for the same machine type and enhance the ability of the model in distinguishing anomalous sound. Moreover, ...
在编码和解码时具有相同的word embedding方式,相反的例子是Optimus,encoder和decoder用了不同的PTM,因此在模型搭建时需要特殊操作。 GPT-2作为VAE的encoder非常强,能够缓解KL collapse现象(1.当VAE的decoder太强了,忽略了encoder输入到 latent space 的信息,变成了“默写式”输出 2. CVAE会加剧KL collapse现象,因为con...
The un-quantized transformer directly uses features from the autoencoder, avoiding information loss from quantization. TransCNN-HAE (Wang et al. 2022) ] is used for blind image inpainting, which addresses the challenges of unknown and various image damage. Unlike existing two-stage approaches, ...
和nlp中的transformer encoder的区别 整体架构上和transformer encoder差异不大,一些细节的差异 (1)输入部分,time series的每个time step的features 相当于一个句子里的一个token的embedding,但是根据实际经验来看,如果每个timestep的features太少做self attention效果不好,这里作者提供的方法是直接用一个shared的linear层来...
2023arxivConvNeXt V2ConvNeXt V2: Co-designing and Scaling ConvNets with Masked AutoencodersCode Interaction Design in Decoder Improved Cross Attention Design YearVenueAcronymPaper TitleCode/Project 2021CVPRSparse R-CNNSparse R-CNN: End-to-End Object Detection with Learnable ProposalsCode ...
For example, the work [29] proposes nonsym- metric deep autoencoder for unsupervised feature learn- ing and presents a deep learning classification model, addressing concerns about NIDS. And the work [30] uses Self-Taught Learning (STL), a deep learning based tech- nique, on the NSL...
Lore, K.G., Akintayo, A., Sarkar, S.: Llnet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recogn. 61, 650–662 (2017) MATH Google Scholar Pizer, S.M., Amburn, E.P., Austin, J.D., Cromartie, R., Geselowitz, A., Greer, T., Haar Romeny, B...
Video GPT is a novel machine learning architecture that employs likelihood-based generative modelling for video synthesis.
To address these limitations, in this paper we propose Hyperspectral Compression Transformer (HyCoT) that is a transformer-based autoencoder for pixelwise HSI compression. Additionally, we apply a simple yet effective training set reduction approach to accelerate the training process. Experimental ...
To overcome this issue, we introduce TABCF, a CF explanation method that leverages a transformer-based Variational Autoencoder (VAE) tailored for modeling tabular data. Our approach uses transformers to learn a continuous latent space and a novel Gumbel-Softmax detokenizer that enables precise ...