Abstract: A computing system includes an encoder that receives an input image and encodes the input image into real image features, a decoder that decodes the real image features into a reconstructed image, a generator that receives first audio data corresponding to the input image and generates first synthetic image features from the first audio data, and receives second audio data and generates second synthetic image features from the second audio data, a discriminator that receives both the real and synthetic image features and determines whether a target feature is real or synthetic, and a classifier that classifies a scene of the second audio data based on the second synthetic image features.

Abstract: A computing system includes an encoder that receives an input image and encodes the input image into real image features, a decoder that decodes the real image features into a reconstructed image, a generator that receives first audio data corresponding to the input image and generates first synthetic image features from the first audio data, and receives second audio data and generates second synthetic image features from the second audio data, a discriminator that receives both the real and synthetic image features and determines whether a target feature is real or synthetic, and a classifier that classifies a scene of the second audio data based on the second synthetic image features.

Abstract: A method for image enhancement on a computing device includes receiving a digital input image depicting a human eye. From the digital input image, the computing device generates a gaze-adjusted image via a gaze adjustment machine learning model by changing an apparent gaze direction of the human eye. From the gaze-adjusted image and potentially in conjunction with the digital input image, the computing device generates a detail-enhanced image via a detail enhancement machine learning model by adding or modifying details. The computing device outputs the detail-enhanced image.