Abstract: A set of images is accessed from a server. The set is analyzed to identify objects in each image using object recognition algorithms. A primary image that includes more of the identified objects than any of the other images of the set is determined. For each of the identified objects in the primary image, a secondary image that includes the identified object and has a higher magnification than the primary image is determined. Links are inserted into the primary image at locations including an identified object that is also in a secondary image. The primary image is displayed and, upon receiving a selection of one of the links, the secondary image that includes the identified object at the location of the selected link is displayed. The set of images is ordered based on relative positions of the identified objects in each image and then linked together based on the ordering.

Abstract: Techniques for training for and determining a confidence of an output of a machine learning model are disclosed. Such techniques include, in some embodiments, receiving, from the machine learning model configured to receive information associated with a data object, information associated with a predicted structure for the data object; encoding, using a second machine learning model, the information associated with the predicted structure for the data object to produce encoded input channels; evaluating, using the second machine learning model, the information associated with the data object with the encoded input channels; and based on the evaluating, determining, using the second machine learning model, a probability of correctness of the predicted structure for the data object.

Abstract: A set of images is accessed from a server. The set is analyzed to identify objects in each image using object recognition algorithms. A primary image that includes more of the identified objects than any of the other images of the set is determined. For each of the identified objects in the primary image, a secondary image that includes the identified object and has a higher magnification than the primary image is determined. Links are inserted into the primary image at locations including an identified object that is also in a secondary image. The primary image is displayed and, upon receiving a selection of one of the links, the secondary image that includes the identified object at the location of the selected link is displayed. The set of images is ordered based on relative positions of the identified objects in each image and then linked together based on the ordering.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating diverse and realistic synthetic documents using deep learning. In particular, the disclosed systems can utilize a trained neural network to generate realistic image layouts comprising page elements that comply with layout parameters. The disclosed systems can also generate synthetic content corresponding to the page elements within the image layouts. The disclosed systems insert the synthetic content into the corresponding page elements of documents based on the image layouts to generate synthetic documents.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating diverse and realistic synthetic documents using deep learning. In particular, the disclosed systems can utilize a trained neural network to generate realistic image layouts comprising page elements that comply with layout parameters. The disclosed systems can also generate synthetic content corresponding to the page elements within the image layouts. The disclosed systems insert the synthetic content into the corresponding page elements of documents based on the image layouts to generate synthetic documents.

Abstract: A set of images is accessed from a server. The set is analyzed to identify objects in each image using object recognition algorithms. A primary image that includes more of the identified objects than any of the other images of the set is determined. For each of the identified objects in the primary image, a secondary image that includes the identified object and has a higher magnification than the primary image is determined. Links are inserted into the primary image at locations including an identified object that is also in a secondary image. The primary image is displayed and, upon receiving a selection of one of the links, the secondary image that includes the identified object at the location of the selected link is displayed. The set of images is ordered based on relative positons of the identified objects in each image and then linked together based on the ordering.

Abstract: A set of images is accessed from a server. The set is analyzed to identify objects in each image using object recognition algorithms. A primary image that includes more of the identified objects than any of the other images of the set is determined. For each of the identified objects in the primary image, a secondary image that includes the identified object and has a higher magnification than the primary image is determined. Links are inserted into the primary image at locations including an identified object that is also in a secondary image. The primary image is displayed and, upon receiving a selection of one of the links, the secondary image that includes the identified object at the location of the selected link is displayed. The set of images is ordered based on relative positons of the identified objects in each image and then linked together based on the ordering.