Abstract: A method for training a machine learning model includes: receiving, by a computer system including a processor and memory, a training data set including imbalanced data; computing, by the computer system, a label density fX(x) in the training data set, computing, by the computer system, a weight function w(x) including a term that is inversely proportional to the label density; weighting, by the computer system, a loss function (x, {circumflex over (x)}) in accordance with the weight function to generate a weighted loss function w(x, {circumflex over (x)}); training, by the computer system, a continuous machine learning model in accordance with the training data set and the weighted loss function w(x, {circumflex over (x)}); and outputting, by the computer system, the trained continuous machine learning model.

Abstract: A method for training a machine learning model includes: receiving, by a computer system including a processor and memory, a training data set including imbalanced data; computing, by the computer system, a label density fX(x) in the training data set, computing, by the computer system, a weight function w(x) including a term that is inversely proportional to the label density; weighting, by the computer system, a loss function (x, {circumflex over (x)}) in accordance with the weight function to generate a weighted loss function w(x, {circumflex over (x)}); training, by the computer system, a continuous machine learning model in accordance with the training data set and the weighted loss function w(x, {circumflex over (x)}); and outputting, by the computer system, the trained continuous machine learning model.

Abstract: A system and method for image testing is configured to apply at least one display property to a test image to generate a display modified test image and applying the at least one display property to a reference image to generate a display modified reference image. The system also applies a human eye model to the display modified test image to generate an eye modified test image and applies the human eye model to the display modified reference image to generate an eye modified reference image. The system may compare the eye modified test image with the eye modified reference image to determine human perceivable differences between the test image and the reference image.

Abstract: A system and method for image testing is configured to apply at least one display property to a test image to generate a display modified test image and applying the at least one display property to a reference image to generate a display modified reference image. The system also applies a human eye model to the display modified test image to generate an eye modified test image and applies the human eye model to the display modified reference image to generate an eye modified reference image. The system may compare the eye modified test image with the eye modified reference image to determine human perceivable differences between the test image and the reference image.

Abstract: A system and method for image testing is configured to apply at least one display property to a test image to generate a display modified test image and applying the at least one display property to a reference image to generate a display modified reference image. The system also applies a human eye model to the display modified test image to generate an eye modified test image and applies the human eye model to the display modified reference image to generate an eye modified reference image. The system may compare the eye modified test image with the eye modified reference image to determine human perceivable differences between the test image and the reference image.

Abstract: A system and method for image testing is configured to apply at least one display property to a test image to generate a display modified test image and applying the at least one display property to a reference image to generate a display modified reference image. The system also applies a human eye model to the display modified test image to generate an eye modified test image and applies the human eye model to the display modified reference image to generate an eye modified reference image. The system may compare the eye modified test image with the eye modified reference image to determine human perceivable differences between the test image and the reference image.