先看下面的图,Transformer是点编码,Itransformer是编码整条序列,Patch TST则是切片。可以看到原始Transformer是通道依赖,而iTransformer和PatchTST则是通道独立。 与已有的模型不同,本文则是通道依赖,而且作者设计了一种多粒度patch切分方法,把从L1-Ln的长度全部切分了一遍,文章称之为跨通道多
好处在于:1、保留更长的前后信息;2、节约transformer计算成本。 那原始的patch是否有不足呢?有的,首先原始patch是对所有的数据集按照相同的片段长度进行切分。但实际情况是不同数据集的采样频率有巨大差距。如下图所示,像汇率数据集、疾病数据集、交通数据集的数据模式显然是不一样的,那么采样频率是分钟的和小时都...
Nevertheless, achieving precise RUL prediction presents significant challenges due to the intricate degradation mechanisms inherent in battery systems and the influence of operational noise, particularly the capacity regeneration phenomena. To address these issues, we propose a novel patch-based transformer (...
之前讲过,放回到LSTM、Transformer时,数据还是要变回三维,因此这里我们把batch和channel合并到一起,数据维度变成了:[(bs*ch), pnum, plen] 此时已经可以放入到模型建模,LSTM输出结果的维度是:[(bs*ch), plen, hidden],然后我们通过线性层和reshape操作,把维度调整回[(bs,pred_len, ch] 代码如下,其实就是维度...
在本文中,我们探讨了针对视觉Transformer的基于决策的黑盒对抗攻击。鉴于ViT不同patch之间噪声敏感性存在较大差异,我们提出了逐patch的噪声去除方法PAR以提升决策攻击的查询效率。PAR同时维护噪声幅度和噪声敏感性掩膜,以逐patch的方式探测和压...
In recent years, attention-based transformer models have sparked significant excitement in natural language processing, and achieved remarkable success in translation, text generation, and language understanding. The advent of transformer not only provides a novel method for sequences modeling, but also ...
Vision Transformer (huge-sized model) 旨在提供图像识别能力。它基于在ImageNet-21k数据集上预训练的Transformer编码器模型,接受224x224分辨率的图像作为输入,通过图像分块和线性嵌入的方式提取图像特征,可用于图像分类等任务。该模型提供预训练的池化层,支持标准化数据操作,并提供统一模型接口,方便用户使用。
CD Projekt RED states that the Transformer model enhances visual stability, lighting, and detail during motion, resulting in a clearer and more detailed visual experience. This option allows players to adjust their graphical settings based on their hardware capabilities and performance pref...
About Code release for "PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting" - PatchMixer/models/Transformer.py at main · tanjingme/PatchMixer
Visual language pre-training (VLP) models have demonstrated significant success in various domains, but they remain vulnerable to adversarial attacks. Addressing these adversarial vulnerabilities is crucial for enhancing security in multi-modal learning. Traditionally, adversarial methods that target VLP model...