Abstract: A computer-implemented method includes obtaining a labeled data set of images including data from a rehearsal memory and new input data, augmenting the labeled data set to generate a first data set and a second data set, wherein each image of the first data set corresponds to a corresponding image of the second data set, inputting the first data set into a query encoder and inputting the second data set into a momentum encoder to obtain encodings output by the query encoder and the momentum encoder, obtaining a contrastive loss based on the encodings using a sum of a first contrastive loss function and a second contrastive loss function, updating parameters of the query encoder based on the obtained contrastive loss, and updating parameters of the momentum encoder based on parameters of the query encoder.

Abstract: The present disclosure relates to an artificial intelligence apparatus capable of classifying and detecting an item of an unseen class having high visual similarity to an item of a seen class that has been learned and known in advance, and a method for detecting an unseen class items thereof, and when the item image is input, generate encoded data by encoding the item image, generate decoded data by decoding the encoded data using a codebook of a plurality of codebooks in which encoded data for each item class is stored, corresponding to a pre-learned item class, and detect the unknown item by classifying the class of the item image based on the generated decoded data.