Abstract: A computer-implemented method of training an image analysis model. The image analysis model comprises a coupling layer that determines an output vector of discrete values from an input vector of discrete values. First, a machine learnable submodel of the coupling layer is trained to predict a second input part of the coupling layer from a first input part of the coupling layer. Next, the image analysis model is trained. This involves applying the coupling layer by applying the machine learnable submodel to the first input part to obtain a prediction of the second input part; and determining a second output part by applying an invertible mapping to the second input part defined by the prediction of the second input part. The mapping maps a predicted value of an element of the second input part to a fixed value independent from the predicted value.

Abstract: A computer-implemented method of training an image analysis model. The image analysis model comprises a coupling layer that determines an output vector of discrete values from an input vector of discrete values. First, a machine learnable submodel of the coupling layer is trained to predict a second input part of the coupling layer from a first input part of the coupling layer. Next, the image analysis model is trained. This involves applying the coupling layer by applying the machine learnable submodel to the first input part to obtain a prediction of the second input part; and determining a second output part by applying an invertible mapping to the second input part defined by the prediction of the second input part. The mapping maps a predicted value of an element of the second input part to a fixed value independent from the predicted value.

Abstract: A computer-implemented method of training an image analysis model. A coupling layer determines an output vector of integer values from an input vector of integer values. The coupling layer is applied by dividing the input vector into non-overlapping first and second input parts; applying a machine learnable submodel of the coupling layer to the first input part to obtain a submodel output of the machine learnable submodel; sampling a transformation vector from a discrete probability distribution, wherein the discrete probability distribution is parameterized based on the submodel output; determining a second output part based on the second input part and the transformation vector; and combining the first input part and the second output part to obtain the output vector. During backpropagation, a gradient of the sampling of the transformation vector is estimated.