Abstract: An autonomous system includes: a first semantic segmentation model trained based on a training dataset including images and labels for the images, the first semantic segmentation model configured to generate a first segmentation map based on an image from a camera; a second semantic segmentation model of the same type of semantic segmentation model as the first semantic segmentation model, the second semantic segmentation model configured to generate a second segmentation map based on the image from the camera; an adaptation module configured to selectively adjust one or more first parameters of the second semantic segmentation model; and a reset module configured to: determine a first total number of unique classifications included in the first segmentation map; determine a second total number of unique classifications included in the first segmentation map; and selectively reset the first parameters to previous parameters, respectively, based on the first and second total numbers.

Abstract: A method for processing a new sample in a data stream for updating a machine learning model configured for performing a task. The machine learning model is implemented by a processor in communication with a memory storing previous samples. The new sample is received, and the machine learning model is trained using combined samples including the new sample and the previous samples. The new sample is stored or not stored in the memory based on distances between the samples in an embedding space learned by the machine learning model.

Abstract: Methods for training a neural network model for sequentially learning a plurality of domains associated with a task. At least one set of auxiliary model parameters is determined by simulating at least one first optimization step based on a set of current model parameters and at least one auxiliary domain associated with a primary domain comprising one or more data points. A set of primary model parameters is determined by performing a second optimization step based on the current model parameters and the primary domain and on the at least one set of auxiliary model parameters and the primary domain and/or the auxiliary domain. The model is updated with the set of primary model parameters.