Abstract: Systems and methods are disclosed for training a previously trained neural network with incremental dataset. Original train data is provided to a neural network and the neural network is trained based on the plurality of classes in the sets of training data and/or testing data. The connected representation and the weights of the neural network is the model of the neural network. The trained model is to be updated for an incremental train data. The embodiments provide a process by which the trained model is updated for the incremental train data. This process creates a ground truth for the original training data and trains on the combined set of original train data and the incremental train data. The incremental training is tested on a test data to conclude the training and to generate the incremental trained model, minimizing the knowledge learned with the original data. Thus, the results remain consistent with the original model trained by the original dataset except the incremental train data.

Abstract: A method of processing data for an artificial intelligence (AI) system includes extracting features of the data to produce a lower dimensional representation of the data points; grouping the lower dimensional representation into clusters using a clustering algorithm; comparing the classes of data points within the clusters; and identifying unrepresented, under-represented, or misrepresented data.

Abstract: Systems and methods are disclosed for training a previously trained neural network with incremental dataset. Original train data is provided to a neural network and the neural network is trained based on the plurality of classes in the sets of training data and/or testing data. The connected representation and the weights of the neural network is the model of the neural network. The trained model is to be updated for an incremental train data. The embodiments provide a process by which the trained model is updated for the incremental train data. This process creates a ground truth for the original training data and trains on the combined set of original train data and the incremental train data. The incremental training is tested on a test data to conclude the training and to generate the incremental trained model, minimizing the knowledge learned with the original data. Thus, the results remain consistent with the original model trained by the original dataset except the incremental train data.

Abstract: The present invention provides a technique for retrieving pictures from a large database that is less complex and uses significantly less memory and computational resources than current techniques. This is accomplished by determining representative data vectors based on a tolerance distance that represents data vectors in a given vector space that defines the picture to extract features of the picture that facilitates in retrieving pictures.

Abstract: The present invention provides a technique for retrieving pictures from a large database that is less complex and uses significantly less memory and computational resources than current techniques. This is accomplished by determining representative data vectors based on a tolerance distance that represents data vectors in a given vector space that defines the picture to extract features of the picture that facilitates in retrieving pictures.

Abstract: The present invention provides a technique for retrieving pictures from a large database that is less complex and uses significantly less memory and computational resources than current techniques. This is accomplished by determining representative data vectors based on a tolerance distance that represents data vectors in a given vector space that defines the picture to extract features of the picture that facilitates in retrieving pictures.