(3) B) Attention-based Encoder-Decoder The attention-based encoder-decoder (AED) model is another type of E2E ASR model [4, 6, 7, 32, 33]. As shown in Figure 1b, AED has an encoder network, an attention module, and a decoder network. The AED model calculates the probability as P...
To solve the above problems, we propose an end-to-end Attention-based Encoder-Decoder Network (AEDNet) which is capable to effectively remove haze while preserving image details well. AEDNet employs a novel channel shuffle attention mechanism to adaptively adjust the weight of each channel-wise ...
To solve the above problems, we propose an end-to-end Attention-based Encoder-Decoder Network (AEDNet) which is capable to effectively remove haze while preserving image details well. AEDNet employs a novel channel shuffle attention mechanism to adaptively adjust the weight of each channel-wise ...
4. Attention-based encoder–decoder models An AED model maps a T-length input sequence O to an L-length output subword sequence C=[c1:L] using an encoder–decoder structure, where the input length is normally longer than the output length. Instead of decomposing into AMs and LMs as in Se...
于注意力的编解码器(attentionbasedencoderdecoder,1相关工作 [5-6][7-8] AED)和换能器(transducers)。这些深度学习模型1.1Conformer编码器 易于搭建、调优,在某些应用场景方面的识别率都超过[15] 由Gulati等提出的Conformer对比文献[9]将卷积 [5] 了基于传统语音识别方法的模型,还可以将多个模型和自我注意相结合...
目前流行的E2E语音方法主要基于以下三种模型构建:连接时序分类(connectionisttemporalclassification,CTC)[3-4]、基于注意力的编解码器(attentionbasedencoderdecoder,AED)[5-6]和换能器(transducers)[7-8]。这些深度学习模型易于搭建、调优,在某些应用场景方面的识别率都超过了基于传统语音识别方法的模型[5],还可以将...
Secondly, in the decoding phase of our encoder–decoder model, a new module called dense upsampling group convolution is designed to tackle the problem of information loss due to stride downsampling. And then, the detailed structural information can be preserved even it is ever destroyed in the ...
Unet [28], Deeplabv3+ [8], MSCI [21], SPGNet [2], RefineNet [22] and DFN [36] adopted encoder-decoder structures that fuse the informa- tion in low-level and high-level layers to predict segmenta- tion mask. SAC [40] and Deformable Convolutional Net- works [11] im...
AEDNet [46] introduces attention into an encoder–decoder network. AMS-Net [47] refines the density map by adding an attention subnetwork. SCLNet [48] incorporates parallel spatial and channel attention in the decoder. Show abstract A benchmark for multi-class object counting and size estimation...
Implementation based on 《 Neural Machine Translation by Jointly Learning to Align and Translate 》 是不是有点眼熟……我的RNN(LSTM)明明就一层,没有第二层Decoder,怎么使用之前的信息呢? AttentionCellWrapper灵感来源 经过我的反复Google,Tensorflow的AttentionCellWrapper并非基于Encoder-Decoder的架构设计的,其灵感...