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
与传统 Auto-encoder:Transformer auto-encoder 通过自注意力机制可以更好地捕获长距离依赖。 与RNN-based Auto-encoder:Transformer 更加并行化,通常在训练和推理时都更快。 7. 优缺点 优点: 可以捕获长距离依赖。 训练和推理速度快。 缺点: 参数量大,需要大量数据来训练。 对于较短的序列,可能会有过拟合的风险...
[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…
2022年，MAE（Masked AutoEncoder）受到语言模型BERT的启发，提出了一种简单而有效的自监督学习方法：随机遮挡输入图像的大部分区域（如75%），然后让模型重建这些被遮挡的区域。这种＂填空＂式的学习方式迫使模型理解图像的语义结构，从而学到更有用的视觉表示。令人惊讶的是，MAE预训练的Vision Transformer在多个分割数...
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...
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...
Meanwhile, we consider these features as discrete and informative “word” tokens and build a transformer-based autoencoder as the reconstruction network. With transformer attention modules, the non-local feature information is well aggregated and the model is less likely to be over-generalized. To ...
The attention mechanisms in Transformers allow for efficient parallel processing, while the recurrent nature of RNNs—often used in sequence-based tasks—leads to slower, sequential processing. Conclusion In this article, you have explored the differences between Transformers and Autoencoders, specifically...
TT 的做法则更类似传统的基于模型的强化学习 (model-based RL) 的规划（planning）方法。在建模方面，它将整个序列中的元素都离散化，然后用了 GPT-2 那样的离散的自回归（auto-regressive）方式来建模整个离线数据集。这使得它能够建模任意给定除去 return-to-go 的序列的后续 。因为建模了后续序列的分布，TT ...
transformer编码器自回归 自编码器重构误差,本文为博主翻译自:Jinwon的VariationalAutoencoderbasedAnomalyDetectionusingReconstructionProbability,如侵立删 http://dm.snu.ac.kr/static/docs/TR/SNUDM-TR-2015-03.pdf 摘要我们提出了一种利用变