MultiModel: Multi-Task Machine Learning Across Domains
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Over the last decade, the application and performance of Deep Learning has progressed at an astonishing rate. However, the current state of the field is that the neural network architectures are highly specialized to specific domains of application. An important question remains unanswered: Will a convergence between these domains facilitate a unified model capable of performing well across multiple domains?
Today, we present MultiModel, a neural network architecture that draws from the success of vision, language and audio networks to simultaneously solve a number of problems spanning multiple domains, including image recognition, translation and speech recognition. While strides have been made in this direction before, namely in Google’s Multilingual Neural Machine Translation System used in Google Translate, MultiModel is a first step towards the convergence of vision, audio and language understanding into a single network.
The inspiration for how MultiModel handles multiple domains comes from how the brain transforms sensory input from different modalities (such as sound, vision or taste), into a single shared representation and back out in the form of language or actions. As an analog to these modalities and the transformations they perform, MultiModel has a number of small modality-specific sub-networks for audio, images, or text, and a shared model consisting of an encoder, input/output mixer and decoder, as illustrated below.
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