Systems and methods for utilizing a machine learning model combining episodic and semantic information to process a new class of data without loss of semantic knowledge

A device, comprising:

• one or more processors configured to:
• receive input data for training a neural network model that includes a semantic deep neural network (DNN) model and an episodic DNN model;
• process the input data, with a convolutional neural network model, to determine output data from the input data;
• process the output data, with the semantic DNN model, to generate semantic output data;
• process the output data, with the episodic DNN model, to generate episodic output data;
• process the semantic output data, with a semantic confidence model, to generate confidence scores for the semantic output data;
• apply weights to the semantic output data and the episodic output data, based on the confidence scores, to generate weighted output data;
• combine the weighted output data to generate final output data;
• generate a trained neural network model based on the final output data;
• and
• perform one or more actions based on the trained neural network model.

A non-transitory computer-readable medium storing instructions, the instructions comprising:

• one or more instructions that, when executed by one or more processors, cause the one or more processors to:
• receive input data to be processed by a neural network model that includes a semantic deep neural network (DNN) model and an episodic DNN model,
• wherein the input data includes a plurality of images, and
• wherein the neural network model processes the input data to detect one or more objects included in the plurality of images;
• process the input data, with a convolutional neural network model, to determine output data from the input data;
• process the output data, with the semantic DNN model, to generate semantic output data;
• process the output data, with the episodic DNN model, to generate episodic output data;
• process the semantic output data, with a semantic confidence model, to generate confidence scores for the semantic output data;
• combine the semantic output data and the episodic output data, based on the confidence scores, to generate final output data;
• and
• perform one or more actions based on the final output data.