Abstract: For classifying products, a method trains a supervised learning product model, wherein the product model embeds product embeddings of a same product close to another product in a latent space of a vector database. The method further generates the vector database of the product embeddings for the plurality of the product images, wherein the vector database comprises product embeddings of known products and unknown products. The method generates a new product embedding for a new product. The method queries the vector database with the new product embedding as a centroid for a proximity query, wherein the new product embedding is a novel distance from other product embeddings in the vector database. The method labels close product embeddings from the vector database as the new product. The method adds the new product to the product detector using product images extracted from within a group of the vector database.

Abstract: For predicting a concept prediction, a processor generates a review job that includes a review question and a corresponding expert concept prediction for an expert media set drawn from a media corpus. The processor generates media reviews from a plurality of reviewers using the review job for the expert media set. The processor extracts media features for the media of the media reviews. The processor trains a review model with the media reviews and corresponding media features. The processor predicts the concept prediction for media of the media corpus using the review model.

Abstract: For predicting a concept prediction, a processor generates a review job that includes a review question and a corresponding expert concept prediction for an expert media set drawn from a media corpus. The processor generates media reviews from a plurality of reviewers using the review job for the expert media set. The processor extracts media features for the media of the media reviews. The processor trains a review model with the media reviews and corresponding media features. The processor predicts the concept prediction for media of the media corpus using the review model.