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

Claim 6:
7. An image classifier configured to classify a first input image, wherein the image classifier is configured to provide an output signal characterizing a classification of the first input image, the image classifier being trained by:
determining a training dataset, wherein the training dataset includes a plurality of second input images; training a normalizing flow using the training dataset, wherein the normalizing flow is configured to predict a first density value based on an input image, wherein the first density value characterizes a likelihood of the 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 of the normalizing flow including, for each second image of the second input images:
determining 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,
determining a second density value based on the output tensor and on the plurality of weights,
determining a natural gradient of the plurality of weights with respect to the second density value, and
adapting the plurality of weights according to the natural gradient;
providing the trained normalizing flow to the image classifier;
providing the image classifier as a trained image classifier; wherein image classifier is configured to predict a first density value for the first input image using the trained normalizing flow;

wherein the image classifier is configured to provide the output signal such that the output signal characterizes a first class when the first density value is below than a predefined threshold; and
wherein the image classifier is configured to provide the output signal such that the output signal characterizes a second class when the first density value is equal to the predefined threshold or above the predefined threshold;
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.