Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention Image denoising is always a challenging task in the field of computer vision and image processing. In this paper, we have proposed an encoder-decoder model with direct attention, which is capable of denoising and ...
Paper:Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting Code: https://github.com/Thinklab-SJTU/Crossformer Crossformer开创性地提出了一种新的层次Encoder-Decoder架构,该架构由左侧的Encoder(灰色)和右侧的Decoder(浅橘色)组成,融合了Dimension-Segment-Wise (DSW)...
也就是,Transformer Encoder对比自注意力最重要的是引入了残差结构,其他都是普通堆叠,尤其是自注意力堆叠成多头。 解码器部分,因为工作时,没法知道右边的token,所以解码器的自注意力要修改为masked 自注意力。 中间的这一块是cross attention. 关于encoder传了两个箭头是因为decoder自己输出的feature要作为q, 而encoder...
通过重复读取由 encoder 产生的源语言句子的表示,注意力机制极大地提高了 NMT 的效果。在本文中,我们提出了一种新的注意力机制,称为「Interactive Attention」,在翻译时,decoder不仅去读取源语言句子的表示,而且还会去修改这些表示。Interactive Attention 可以记录 decoder 和源语言句子表示之间的交互过程,因此提高了翻译...
这一章我们来聊聊skip-thought的三兄弟，它们在解决skip-thought遗留问题上做出了不同的尝试【Ref1～4】, 以下paper可能没有给出最优的解决方案(对不同的NLP任务其实没有最优只有最合适）但它们提供了另一种思路和可能性。上一章的skip-thought有以下几个值得进一步探讨的点 Q1 RNN计算效率低：Encoder-Decoder都...
自编码器由两个模块—编码器(encoder)和解码器(decoder)组成。编码器负责输入原始样本,输出压缩编码(code);解码器负责输入编码,还原出原始样本。解码还原出来的样本跟真正的原始样本进行比较,可以计算重构误差,自编码器的训练目标就是尽可能地减少这个重构误差。
接着,执行Multi-Head Attention,接着把Attention Layer的输入\(a\)和输出\(b\)相加,得到\({b}'\),再对\({b}'\)做一个Layey Norm,然后再通过Feed Forward然后再做Add&Norm,把这整个过程重复N次,最终得到Encoder的输出结果 在Decoder中,我们使用了Masked Multi-Head Attention,这里的解释是: ...
This paper proposes a novel hierarchical temporal attention-based LSTM encoder-decoder model for individual location sequence prediction. The proposed hierarchical attention mechanism captures both long-term and short-term dependencies underlying in individual longitudinal trajectories, and uncovers frequential ...
this paper proposes an approach using the long short-term memory (LSTM) encoder-decoder network with attention mechanism. First, the approach extracts the sequential and contextual features of the historical workload data through the encoder network. Second, the model integrates the attention mechanism...
考虑只有encoder差别比较大,索性把CNN-LSTM和上一章的skip-thought放一块了,只对encoder/decoder的cell选择做了区分。这里只给出CNN Encodere的实现,bridge的部分是参考了google的seq2seq,完整代码看这里Github-Embedding-skip_thought defcnn_encoder(input_emb, input_len, params):# batch_szie * seq_len * ...