MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning

• URL: http://arxiv.org/abs/2401.02744v1
• Date: Fri, 5 Jan 2024 10:41:55 GMT
• Title: MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning
• Authors: Alfirsa Damasyifa Fauzulhaq, Wahyu Parwitayasa, Joseph Ananda Sugihdharma, M. Fadli Ridhani, Novanto Yudistira
• Abstract summary: Neuron labeling is an approach to visualize the behaviour and respond of a certain neuron to a certain pattern that activates the neuron. Previous work, namely MILAN, 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. 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.
• Score: 1.7243216387069678
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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