Summary of Mami: Multi-attentional Mutual-information For Long Sequence Neuron Captioning, by Alfirsa Damasyifa Fauzulhaq et al.
MAMI: Multi-Attentional Mutual-Information for Long Sequence Neuron Captioning
by Alfirsa Damasyifa Fauzulhaq, Wahyu Parwitayasa, Joseph Ananda Sugihdharma, M. Fadli Ridhani, Novanto Yudistira
First submitted to arxiv on: 5 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes an improved approach to neuron labeling, a technique for visualizing the behavior and response of individual neurons in deep neural networks. Building on previous work, MILAN (Mutual Information-guided Linguistic Annotation of Neuron), the authors aim to enhance its performance by incorporating different attention mechanisms and combining their advantages. The proposed model uses an encoder-decoder architecture with a pretrained CNN-based model as the encoder and an RNN-based model for text generation. Experimental results on a compound dataset demonstrate the effectiveness of this approach, achieving higher BLEU and F1-Score scores compared to previous methods. Specifically, the authors report BLEU and F1-Score scores of 17.742 and 0.4811, respectively, as well as peak performance metrics at convergence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves how we understand what individual neurons in a deep learning network are doing. Neuron labeling is like taking an X-ray of the brain to see which parts are working together. The researchers took an existing approach called MILAN and made it better by adding new techniques for focusing on important details. They tested their new model on lots of data and found that it performed much better than before, especially at finding the most important information. |
Keywords
» Artificial intelligence » Attention » Bleu » Cnn » Deep learning » Encoder » Encoder decoder » F1 score » Rnn » Text generation
