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
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 ...
[Semi-Supervised] Transformer-based Conditional Variational Autoencoder for Controllable Story Generation (Arxiv 2021)原文地址:https://arxiv.org/abs/2101.00828 原文代码:https://github.com/fangleai…
Leveraging these two trends, we introduce Regularized Latent Space Optimization (ReLSO), a deep transformer-based autoencoder, which features a highly structured latent space that is trained to jointly generate sequences as well as predict fitness. Through regularized prediction heads, ReLSO introduces...
ViV-Ano: anomaly detection and localization combining vision transformer and variational autoencoder in the manufacturing process. Electronics 11, 2306 (2022). Article Google Scholar Oquab, M. et al. Dinov2: Learning robust visual features without supervision. Preprint at https://arxiv.org/abs/...
This repository contains source code for paper Transformer-based Conditional Variational Autoencoder for Controllable Story Generation: @article{fang2021transformer, title={Transformer-based Conditional Variational Autoencoder for Controllable Story Generation}, author={Fang, Le and Zeng, Tao and Liu, Chao...
(Wang et al.2022) introduced a one-stage Transformer-CNN Hybrid AutoEncoder designed to combine the strengths of global contextual modeling from Transformers and local feature extraction from CNNs; it operates at 19.71G FLOPS and 2.75M parameters. In contrast, CBNet (Jin et al.2023) enhanced ...
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 ...
In this study, the RETFound model, a ViT-based deep learning framework, was employed for metabolic syndrome prediction from retinal images. This model leverages a self-supervised learning approach using Masked AutoEncoder (MAE)68, which is trained without human-crafted labels. It learns patterns ...
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...