Abstract: A device for machine learning is provided, including a first neural network layer, a second neural network layer with a normalization layer arranged in between. The normalization layer is configured to, when the device is undergoing training on a batch of training samples, receive multiple outputs of the first neural network layer for a plurality of training samples of the batch, each output comprising multiple data values for different indices on a first dimension and a second dimension; group the outputs into multiple groups based on the indices on the first and second dimensions; form a normalization output for each group which are provided as input to the second neural network layer. According to the application, the training of a deep convolutional neural network with good performance that performs stably at different batch sizes and is generalizable to multiple vision tasks is achieved, thereby improving the performance of the training.

Abstract: A computer system and method are provided for training a machine learning system to perform a classification task by classifying input data into one of a plurality of classes. The system is configured to: receive per class training data from which per class representations can be derived, wherein each class is described by multiple representations; process the training data to form, for at least one class, a first proxy for a relatively global portion of an item of training data and multiple proxies for distinct relatively local portions of the item of training data, each proxy corresponding to a representation of the data belonging to that class.

Abstract: A computer implemented method and system for training a machine to identify a target within video data, the method comprising the steps of providing a training data set including identified labelled targets within video data having the same target within different video views. Generating, using a learning model, a bounding box action policy for determining required adjustments to a bounding box around a target in the video data by: generating a bounding box around a labelled target within a first view of the video data. Converting the target bounded by the bounding box to a quantitative representation. Determining a matching level between the quantitative representation and a quantitative representation of a further labelled target within the video data from a second view different to the first view. Looping the following steps one or more times, the looped steps comprising: using the bounding box action policy to determine an action to change the bounding box.