Patent ID: 11961275
Assignee: ROBERT BOSCH GMBH
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 7:
8. A training system configured to train a normalizing flow, wherein the normalizing flow is configured to predict a first density value based on a first input image, wherein the first density value characterizes a likelihood of the first input image to occur, wherein the first density value is predicted based on an intermediate output of a first convolutional layer of the normalizing flow, and wherein the intermediate output is determined based on a plurality of weights of the first convolutional layer, the training system configured to:
determine a second input image;
determine an output tensor, wherein the output is determined by providing the second input image to the normalizing flow and providing an output of the normalizing flow as the output tensor;
determine a second density value based on the output tensor and on the plurality of weights;
determine a natural gradient of the plurality of weights with respect to the second density value; and
adapt the plurality of weights according to the natural gradient;
wherein the natural gradient is determined according to the formula:

∇w(l)=δl*(w(l)*Th(l))T+w(l)·Hl·Wl,, wherein ∇w(l) is the natural gradient, δl is an error signal for the first convolutional layer, w(l) is the plurality of weights, Hl is a height of a layer input of the first convolutional layer, Wl is a width of the layer input, * denotes a convolution operation and *T denotes a transposed convolution operation.