Abstract: In some embodiments, a method receives query content that includes first visual data and select reference content that includes second visual data. A first visual feature mapping for the query content is generated using a model that is trained to generate visual feature mappings using content pairs where alterations in one instance of the content pair are used in the training. A second visual feature mapping is determined for the reference content. The second visual feature mapping is determined using the model. The method compares the first visual feature mapping and the second visual feature mapping to generate a score that rates a similarity between the query content and the reference content. The score is based on recognizing an alteration in one instance of the query content and the reference content. The score is output.

Abstract: In some embodiments, a method receives a text prompt and executes a text encoder on the text prompt to generate an embedding representation. A set of base images is generated based on the embedding representation and parameters of a base image generation model. A high resolution model is executed to upsample one or more base images in the set of base images based on parameters of the high resolution model to generate a set of final images. The method ranks the set of base images or the set of final images using reward values that are generated by a reward model. The reward model is trained using human input that provided feedback on a quality of generated images using the base image generation model and the high resolution model. One or more final images are output based on the ranking in response to the text prompt.

Abstract: A system stores a plurality of data models, each data model being configured to sort datasets based on a set of criteria unique to the data model. The system further identifies a plurality of content streams on a plurality of content streaming platforms. The system then executes each data model of the plurality of data models on each content stream of the plurality of content streams to generate a labeled content file of each content stream of the plurality of content streams.

Abstract: A system executes a logo detection data model that processes, using computer processing resources, content data to detect a plurality of logos in the content data. The logo detection data model further automatically labels each detected logo of the plurality of logos in the content data.