Abstract: A self-driving method and a related apparatus, the method including determining, by a self-driving apparatus, a task feature vector of a self-driving task according to M groups of historical paths of the self-driving task, where the task feature vector is a vector representing features of the self-driving task, and where M is an integer greater than 0, determining, by the self-driving apparatus, according to the task feature vector and a status vector, a target driving operation that needs to be performed, where the status vector indicates a driving status of the self-driving apparatus, and performing, by the self-driving apparatus, the target driving operation.

Abstract: The present disclosure relates to a method comprising a training system iteratively training a machine learning algorithm using current training data. The current training data comprises a local dataset of a current task and a replay dataset and may be updated for a next iteration as follows. A training dataset may be received. If the training dataset is not a shared dataset and its task is different from the current task: information representing the local dataset may be shared with other training systems, the local dataset may be added to the replay dataset, and the received training dataset may be used as the local dataset for a next iteration. In case the task is the current task: the received training dataset may be added to the local dataset. If the training dataset is a shared dataset, the received training dataset may be added to the replay dataset.

Abstract: Embodiments of the present disclosure provide a method, a device, and a computer program product for updating a model. The method includes: determining a performance metric of a trained machine learning model at runtime; determining a homogeneity degree between a verification data set processed by the machine learning model at runtime and a training data set used to train the machine learning model; determining a type of a conceptual drift of the machine learning model based on the performance metric and the homogeneity degree; and performing an update of the machine learning model based on the type of the conceptual drift, where the update includes a partial update or a global update. In this way, a desired performance of the machine learning model can be maintained, while avoiding excessive time costs and computational resource costs caused by frequent global updates.

Abstract: Systems/techniques that facilitate generation of model suitability coefficients are provided. In various embodiments, a system can access a model trained on a training dataset, and the system can compute a coefficient indicating whether the model is suitable for deployment on a target dataset, based on analyzing activation maps associated with the model. In some cases, the system can: train a generative adversarial network (GAN) to learn a distribution of training activation maps produced by the model; generate a set of target activation maps of the model, by feeding samples from the target dataset to the model; cause a generator of the GAN to generate synthetic training activation maps from the learned distribution of training activation maps; iteratively perturb inputs of the generator until distances between the synthetic training activation maps and the target activation maps are minimized; and aggregate the minimized distances to form the coefficient.