Decoding Diverse Solutions from Neural Sequence Models
Beam search, the standard work-horse for decoding outputs from neural sequence models like RNNs produces generic and uninteresting sequences. This is inadequate for AI tasks with inherent ambiguity — for example, there can be multiple correct ways of describing the contents of an image. To overcome this we propose a diversity-promoting replacement, Diverse Beam Search that produces sequences that are significantly different — with runtime and memory requirements comparable to beam search.
Arxiv Paper link: https://arxiv.org/abs/1610.02424
Diverse Beam Search Demo: http://dbs.cloudcv.org/captioning
We use RabbitMQ to queue the submitted jobs. Also, we use Redis as backend for realtime communication using websockets.
All the instructions for setting diverse beam search from scratch can be found here
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