Dropout during inference as a model for neurological degeneration in an image captioning network
This work addresses modeling neurological degeneration for researchers in computational neuroscience or AI, but it is incremental as it applies an existing technique to a new context.
The study introduced dropout during inference in an image captioning network to simulate neurodegenerative diseases like Alzheimer's and Wernicke's aphasia, finding that a dropout rate of 0.4 best approximated the training corpus's word frequency distribution.
We replicate a variation of the image captioning architecture by Vinyals et al. (2015), then introduce dropout during inference mode to simulate the effects of neurodegenerative diseases like Alzheimer's disease (AD) and Wernicke's aphasia (WA). We evaluate the effects of dropout on language production by measuring the KL-divergence of word frequency distributions and other linguistic metrics as dropout is added. We find that the generated sentences most closely approximate the word frequency distribution of the training corpus when using a moderate dropout of 0.4 during inference.