Method and apparatus for training artificial intelligence based on episode memory

An apparatus for training artificial intelligence based on an episodic memory, the apparatus comprising:

• a full memory comprising a feature vector associated with a training dataset;
• and
• a processor configured to control the full memory,
• wherein the processor is further configured to:
• construct an episodic memory to include a representation of the feature vector;
• obtain output data by inputting query data into an artificial intelligence model;
• derive a similarity between the output data and the representation in the episodic memory;
• and
• derive an episode loss function based on the similarity,
• wherein the feature vector is generated by inputting the training dataset into a pretrained deep neural network and includes a plurality of numerical elements,
• wherein the processor is configured to repeatedly construct the episodic memory, obtain the output data, derive the similarity, and derive the episode loss function a plurality of times during training to iteratively update one or more parameters of the artificial intelligence model using backpropagation based on the episode loss function and an optimization algorithm, and
• wherein, after each iteration of the training, the processor is configured to update the feature vector stored in the full memory by:
• inputting at least a portion of the training dataset into the artificial intelligence model after the one or more parameters are updated to generate updated output data;
• generating an updated feature vector based on the updated output data;
• and
• storing the updated feature vector in the full memory in association with the training dataset prior to constructing the episodic memory for a next iteration of the training.

A non-transitory computer-readable medium storing a computer program, the computer program implementing:

• constructing an episodic memory to include a representation of a feature vector stored in a full memory, the feature vector being associated with a training dataset;
• obtaining output data by inputting query data into an artificial intelligence model;
• deriving a similarity between the output data and the representation in the episodic memory;
• and
• deriving an episode loss function based on the similarity,
• wherein the feature vector is generated by inputting the training dataset into a pretrained deep neural network and includes a plurality of numerical elements,
• wherein constructing the episodic memory, obtaining the output data, deriving the similarity, and deriving the episode loss function are repeated a plurality of times during training to iteratively update one or more parameters of the artificial intelligence model using backpropagation based on the episode loss function and an optimization algorithm, and
• wherein, after each iteration of the training, the computer program is configured to update the feature vector stored in the full memory by:
• inputting at least a portion of the training dataset into the artificial intelligence model after the one or more parameters are updated to generate updated output data;
• generating an updated feature vector based on the updated output data;
• and
• storing the updated feature vector in the full memory in association with the training dataset prior to constructing the episodic memory for a next iteration of the training.