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In this paper, we investigate Discrete Wavelet Transform (DWT) in the frequency domain and design a new Wavelet-Attention (WA) block to only implement attention in the high-frequency domain. Based on this, we propose a Wavelet-Attention convolutional neural network (WA-CNN) for image ...
在深度学习中,首先在《Neural Machine Translation by Jointly Learning to Align and Translate》中提出了注意力机制,通过使用基于注意力的编解码器在源句子中选择参考词。注意机制帮助解码器部分基于所有输入状态(不仅仅是最后一个状态)的加权组合来输出字。 如下图所示,注意力模型将 个状态 和上下文 作为输入,接下...
KDD 2023 | WHEN异构时间序列分析模型:当Wavelet和DTW遇上Attention。首次解决时序分析的异质性问题!附原文, 视频播放量 382、弹幕量 0、点赞数 7、投硬币枚数 0、收藏人数 18、转发人数 8, 视频作者 二范数智能, 作者简介 专注AI教育。创始团队来自阿里巴巴,在人工智能
attention module. Next, we test wavelet transform as a standalone channel compression method. We prove that global average pooling is equivalent to the recursive approximate Haar wavelet transform. With this proof, we generalize channel attention using Wavelet compression and name it WaveNet. ...
Apache-2.0 license [ICIP 2022]Half Wavelet Attention on M-Net+ for Low-light Image Enhancement Chi-Mao Fan, Tsung-Jung Liu, Kuan-Hsien Liu Abstract : Low-Light Image Enhancement is a compute vision task which reinforces the dark images to appropriate brightness. It can also be seen as an...
为了应对上述挑战,论文提出了 Wavelet-DTW Hybrid attEntion Networks(WHEN),用于对异质时间序列的分析。WHEN 本质上是一个混合注意力网络,将小波变换和动态时间规整算法通过注意力机制进行整合。WHEN 的框架如图 2 所示,包含两个核心模块。 小波注意力(WaveAtt)模块的关键组件是与局部数据相关的小波函数,其中小波函数的...
WLSAN: Wavelet-Layer-Spatial Attention Network for Single Image Super Resolution Single Image Super Resolution has been a hot topic in recent years, which has wide application prospects. Some recent works use attention in SR to capture ... R Peng,Y Su,B Yu,... - 《Highlights in Science ...
ERPs-Based Attention Analysis Using Continuous Wavelet Transform: the Bottom-up and Top-down ParadigmsEvoked Related Potentials (ERPs) analysis for distinguishing between bottom-up and top-down attention, using 256-channel EEG signals obtained by measurements on humans, is investigated here. The......
kernel_init=nn.initializers.xavier_uniform(), bias_init=nn.initializers.normal(stddev=1e-6), use_bias=False, broadcast_dropout=False, dropout_rate=self.attention_dropout_rate, decode=False)(z[level], deterministic=deterministic) z = wavspa.waverec_learn(z, wavelet)[:,:inputs.shape[1],:]...