DATA CANDIDATE QUERYING VIA EMBEDDINGS FOR DEEP LEARNING REFINEMENT

Systems/techniques that facilitate data candidate querying via embeddings for deep learning refinement are provided. In various embodiments, a system can access a test data candidate provided by a client, generate, via a first deep learning neural network, an inferencing output based on the test data candidate, and access feedback indicating whether the client accepts or rejects the inferencing output. In various aspects, the system can generate, via at least one second deep learning neural network, at least one embedding based on the test data candidate. In various instances, the system can, in response to the feedback indicating that the client rejects the inferencing output, identify, in a candidate-embedding dataset, one or more data candidates whose embeddings are within a threshold level of similarity to the at least one embedding and can retrain the first deep learning neural network based on the one or more data candidates.