MAMI：用于长序列神经元描述的多注意力互信息方法
MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning
AI总结：
针对MILAN在长序列神经元描述上表现不佳的问题，本文提出MAMI方法，通过融合多种注意力机制提升性能，在复合数据集上取得了更高的BLEU和F1-Score。
摘要：
神经元标记是一种可视化特定神经元对激活该神经元的特定模式的行为和响应的方法。神经元标记提取有关深度神经网络中特定神经元捕获的特征的信息，其中一种方法是使用编码器-解码器图像描述方法。所使用的编码器可以是预训练的基于CNN的模型，而解码器是基于RNN的文本生成模型。先前的工作，即MILAN（神经元互信息引导的语言标注），尝试在编码器中使用修改后的Show, Attend, and Tell (SAT) 模型，并在解码器中使用添加了Bahdanau注意力的LSTM来可视化神经元行为。MILAN在短序列神经元描述上能展现出很好的结果，但在长序列神经元描述上表现不佳，因此在这项工作中，我们希望通过利用不同类型的注意力机制，并将多个注意力结果相加为一个，来进一步提升MILAN的性能，从而结合多种注意力机制的所有优势。使用我们的复合数据集，我们在提出的模型上获得了更高的BLEU和F1-Score，分别达到17.742和0.4811。在模型达到峰值收敛的某个时刻，我们的模型获得了21.2262的BLEU和0.4870的BERTScore F1-Score。
英文摘要：
Neuron labeling is an approach to visualize the behaviour and respond of a certain neuron to a certain pattern that activates the neuron. Neuron labeling extract information about the features captured by certain neurons in a deep neural network, one of which uses the encoder-decoder image captioning approach. The encoder used can be a pretrained CNN-based model and the decoder is an RNN-based model for text generation. Previous work, namely MILAN (Mutual Information-guided Linguistic Annotation of Neuron), has tried to visualize the neuron behaviour using modified Show, Attend, and Tell (SAT) model in the encoder, and LSTM added with Bahdanau attention in the decoder. MILAN can show great result on short sequence neuron captioning, but it does not show great result on long sequence neuron captioning, so in this work, we would like to improve the performance of MILAN even more by utilizing different kind of attention mechanism and additionally adding several attention result into one, in order to combine all the advantages from several attention mechanism. Using our compound dataset, we obtained higher BLEU and F1-Score on our proposed model, achieving 17.742 and 0.4811 respectively. At some point where the model converges at the peak, our model obtained BLEU of 21.2262 and BERTScore F1-Score of 0.4870.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
