[jessestcharles]

One of the fantastic qualities of embedding based language models is that they provide a view on a semantic space that can be used quantitatively in most any downstream language task. As a conversational intelligence company Frame has many products that are enhanced by having a high quality domain specific language model to build on: tagging, sentiment, topic extraction, key words, summarization, etc. Best of all, these products can be iterated on in parallel! Improvements in a language model’s representation of a body of text should improve all downstream task without modification.

[mlthoughts2018]

The same thing is also true for computer vision models. A core deep network usually trained either with a dual embedding of associated text against search ranking or trained to predict tags or labels. The output network may be of limited use on the original training task but ends up producing an excellent embedding model by extracting the neurons from some deep layer.

You start automatically encoding your entire image collection and incoming images into that embedding model and rely on it as a lingua franca on which to base all sorts of other companion models like object detection, face recognition, gender/age/ethnicity prediction, spam detection, aesthetic / composition appraisal, caption generation, style transfer etc etc.

[nateburke]

Wow! Sounds like you're getting a ton of leverage here.

What kinds of measures do you have in place to prevent implicit bias against protected classes from creeping into your training sets?