Contrastive Neural Network Training in an Active Learning Environment

A computer program product to train a contrastive neural network (CNN) in an active learning environment, the computer program product comprising a computer readable storage medium having program code embodied therewith, the program code executable by a processor to:

• feed a historical dataset into a neural network, including the program code to select the neural network and pre-train the selected neural network with labeled data in the historical dataset, the pre-training including training weights of layers of the selected neural network;
• train the CNN for a new dataset, including the program code to transfer knowledge from the trained weights of the selected neural network, including:
• borrow weights of a knowledge operator from the pre-trained neural network as a regularizer;
• learn features novel to the new dataset, including update weights of the knowledge operator;
• and
• combine the borrowed weights with the updated weights;
• and
• leverage the CNN to predict one or more labels for the dataset as output data.

A method comprising:

• feeding a historical dataset into a neural network, including selecting the neural network and pre-training the selected neural network with labeled data in the historical dataset, the pre-training including training weights of layers of the selected neural network;
• training a contrastive neural network (CNN) in an active learning environment for a new dataset while transferring knowledge from the trained weights of the selected neural network, including:
• borrowing weights of a knowledge operator from the pre-trained neural network;
• learning features novel to the new dataset, including updating weights of the knowledge operator;
• and
• combining the borrowed knowledge operator weights with the updated weights
• the CNN predicting one or more labels for the new data as output data.