mathena

Am I really missing something here or this thing is a complete nonsense with no actual use cases what's so ever in practice?

There are a number of off-the-shelf models that would give you image/sentence embedding easily. Anyone with sufficient understanding of embedding/word2vec would have no trouble train an embedding that is catered to the specific application, with much better quality.

For NLP applications, the corpus quality dictates the quality of embedding if you use simple W2V. Word2Vec trained on Google News corpus is not gonna be useful for chatbot, for instance. Different models also give different quality of embedding. As an example, if you use Google BERT (bi-directional LSTM) then you would get world-class performance in many NLP applications.

The embedding is so model/application specific that I don't see how could a generic embedding would be useful in serious applications. Training a model these days is so easy to do. Calling TensorFlow API is probably easier then calling Basilica API 99% of the case.

I'd be curious if the embedding is "aligned", in the sense that an embedding of the word "cat" is close to the embedding of a picture of cat. I think that would be interesting and useful. I don't see how Basilica solve that problem by taking the top layers off ResNet though.

I appreciate the developer API etc, but as an ML practitioner this feels like a troll.