Abstract: There is provided a method and system for training and adapting a set of trained models each comprising a common feature extractor by using an unlabelled training dataset to thereby obtain an updated common feature extractor. The set of trained models have been trained for a common prediction task on a labelled training dataset is obtained. An unlabelled training dataset is obtained. The set of trained models is trained by using the unlabelled training dataset by generating, using the common feature extractor, a set of feature vectors for the unlabelled training dataset, and generating, using the set of trained models, a set of predictions, the set of predictions comprising a respective prediction for each of the set of feature vectors. During training, the common feature extractor is updated by maximizing, for each given trained model, a mutual information between the set of feature vectors and the respective predictions.

Abstract: A unit is disclosed for generating combined feature maps in accordance with a processing task to be performed, the unit comprising a feature map generating unit for receiving more than one modality and for generating more than one corresponding feature map using more than one corresponding transformation; wherein the generating of each of the more than one corresponding feature map is performed by applying a given corresponding transformation on a given corresponding modality, wherein the more than one corresponding transformation is generated following an initial training performed in accordance with the processing task to be performed and a combining unit for selecting and combining the corresponding more than one feature map generated by the feature map generating unit in accordance with at least one combining operation and for providing at least one corresponding combined feature map; wherein the combining unit is operating in accordance with the processing task to be performed and the combining operation

Abstract: A unit is disclosed for generating combined feature maps in accordance with a processing task to be performed, the unit comprising a feature map generating unit for receiving more than one modality and for generating more than one corresponding feature map using more than one corresponding transformation; wherein the generating of each of the more than one corresponding feature map is performed by applying a given corresponding transformation on a given corresponding modality, wherein the more than one corresponding transformation is generated following an initial training performed in accordance with the processing task to be performed and a combining unit for selecting and combining the corresponding more than one feature map generated by the feature map generating unit in accordance with at least one combining operation and for providing at least one corresponding combined feature map; wherein the combining unit is operating in accordance with the processing task to be performed and the combining operation

Abstract: A method and a system are disclosed for generating synthetically anonymized data, the method comprising providing first data to be anonymized; providing a data embedding comprising data features, wherein data features enable a representation of corresponding data, and wherein the data is representative of the first data; providing an identifier embedding comprising identifiable features, wherein the identifiable features enable an identification of the data and the first data; providing a task-specific embedding comprising task-specific features, wherein said task-specific features enables a disentanglement of different classes relevant to the given task; generating synthetically anonymized data, the generating comprising a generative process using samples comprising a first sampling from the data embedding which ensures that a corresponding first sample originates away from a projection of the data and the first data in the identifier embedding and a second sampling from the task-specific embedding which ens

Abstract: A unit is disclosed for generating combined feature maps in accordance with a processing task to be performed, the unit comprising a feature map generating unit for receiving more than one modality and for generating more than one corresponding feature map using more than one corresponding transformation; wherein the generating of each of the more than one corresponding feature map is performed by applying a given corresponding transformation on a given corresponding modality, wherein the more than one corresponding transformation is generated following an initial training performed in accordance with the processing task to be performed and a combining unit for selecting and combining the corresponding more than one feature map generated by the feature map generating unit in accordance with at least one combining operation and for providing at least one corresponding combined feature map; wherein the combining unit is operating in accordance with the processing task to be performed and the combining operation