We propose a spatial-temporal video compression network (STVC) using the spatial-temporal priors with an attention module (STPA). On the one hand, joint spatial-temporal priors are used for generating latent representations and reconstructing compressed outputs because efficient temporal and spatial ...
生成图片的时候Transformer会按照一定大小的窗口计算滑动attention,按照自回归的方式生成下一个patch的code id。 ViT-VQ-GAN 《Vector-Quantized Image Modeling With Improved VQ-GAN》是Google DeepMind在ICLR 2022提出来的一个工作,对VQ-GAN作了进一步的改进,主要包括两个地方,第一个是直接用ViT提取图像特征,第二是...
LSTM-autoencoder with attentions for multivariate time seriesThis repository contains an autoencoder for multivariate time series forecasting. It features two attention mechanisms described in A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction and was inspired by Seanny123's ...
更重要的是,这个过程的中间产物-隐空间,相较于像素空间,能够以很小的特征空间来表征图片,可以迁移到attention机制底座的模型训练的下流任务,比如本文的主题:Stable Diffusion。 def reconstruct_with_vqgan(x, model): # could also use model(x) for reconstruction but use explicit encoding and decoding here z...
There has been a lot of previous works on speech emotion with machine learning method. However, most of them rely on the effectiveness of labelled speech data. In this paper, we propose a novel algorithm which combines both sparse autoencoder and attention mechanism. The aim is to benefit fro...
pytorch cross attention代码 pytorch autoencoder 在图像分割这个问题上,主要有两个流派:Encoder-Decoder和Dialated Conv。本文介绍的是编解码网络中最为经典的U-Net。随着骨干网路的进化,很多相应衍生出来的网络大多都是对于Unet进行了改进但是本质上的思路还是没有太多的变化。比如结合DenseNet 和Unet的FCDenseNet, Unet...
Cross-modal retrieval has become a topic of popularity, since multi-data is heterogeneous and the similarities between different forms of information are worthy of attention. Traditional single-modal methods reconstruct the original information and lack of considering the semantic similarity between differen...
Although the training of Autoencoders is similar to regular Neural Networks, some hyperparameters require special attention, like: • Embedding size: the size of the latent representation represents a trade-off between compression and accurate reconstruction. Smaller size results in more compression, ...
Autoencoder with fast.ai What's an autoencoder?¶ An autoencoder is a machine learning system that takes an input and attempts to produce output that matches the input as closely as possible. This useless and simple task doesn't seem to warrant the attention of machine learning (for ...
However, this sampling process requires some extra attention. When training the model, we need to be able to calculate the relationship of each parameter in the network with respect to the final output loss using a technique known as backpropagation. However, we simply cannot do...