Abstract: An apparatus and method with out-of-distribution data detection is provided. The apparatus includes one or more processors configured to execute instructions; and one or more memories storing the instructions, wherein, the execution of the instructions by the one or more processors configures the one or more processors to generate output data using a neural network model provided input data; determine, based on the output data, a maximum loss value among calculated loss values that correspond to neighboring data within a reference distance from the input data; and detect, based on the maximum loss value and a threshold, whether the input data is out-of-distribution (OOD) data that is different from in-distribution data corresponding to training data used in a training of the neural network model.

Abstract: A method includes obtaining a color transformation matrix that removes color distortion of an original color histogram of a target color space for an input image of an original color space; obtaining a color transformation image of the original color space from which color distortion of the input image is removed using the color transformation matrix; and obtaining a space transformation image with a minimum target loss value between an output vector of a neural network for one of candidate images and each of a plurality of candidate class vectors of the input image, the candidate images being spaced apart from the color transformation image of the original color space by a distance less than a threshold.

Abstract: A method includes obtaining a color transformation matrix that removes color distortion of an original color histogram of a target color space for an input image of an original color space; obtaining a color transformation image of the original color space from which color distortion of the input image is removed using the color transformation matrix; and obtaining a space transformation image with a minimum target loss value between an output vector of a neural network for one of candidate images and each of a plurality of candidate class vectors of the input image, the candidate images being spaced apart from the color transformation image of the original color space by a distance less than a threshold.