Adobe, Inc.

Nipun Poddar

Naveen Prakash Goel

Sumeet Khurana

Amit Gupta

Amit Mittal

Vipul Aggarwal

Abhishek Kumar Pandey

David D Barnes

Rebecca E Hauser

Pranjal Bhatnagar

Anas Lnu

Neha Pant

Shvet Chakra

Methods and systems are provided for facilitating implementation of machine learning models in embedded software. In embodiments, a lean machine learning model, having a limited number of layers, is trained in association with a complex machine learning model, having a greater number of layers. To this end, a complex machine learning model, having a first number of layers, can be trained based on an output generated from a lean machine learning model used as input to the complex machine learning model. Further, the lean machine learning model, having a second number of layers less than the first number of layers, is trained using a loss value generated in association with training the complex machine learning model. Thereafter, the trained lean machine learning model can be provided for implementation in an embedded software.

Facilitating implementation of machine learning models in embedded software

Spot aware print workflow techniques and system are described. In an implementation, a digital document is received for printing that includes a plurality of objects. Spot functionality is detected as corresponding to a respective object based on object properties detected for the respective object. One or more spot planes for are generated based on the spot functionality and a determination is made of color values for the one or more spot planes, respectively, based on context data describing a context, in which, the one or more spot planes are to be printed. The spot planes having the color values are output for printing by a print mechanism.

Automated content and context aware spot print workflow

The present disclosure relates to systems, non-transitory computer-readable media, and methods for utilizing intelligent sectioning and selective document reflow for section-based printing. For example, the disclosed systems can intelligently identify document objects (e.g., document structures and sections) within a digital document by utilizing a machine-learning model. In so doing, the disclosed systems can identify document-object types and document-object locations for the document objects in the digital document. In turn, the disclosed systems can provide, for display within a dynamic printing interface, selectable document sections comprising the identified document objects. In response to a user selection of one or more of the selectable document sections, the disclosed system can generate a modified digital document for printing by reflowing the identified document objects in accordance with the user selection. In some cases, reflowing comprises removing unselected document objects and/or repositioning one or more of the selected document objects.

Utilizing intelligent sectioning and selective document reflow for section-based printing

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Noida, India

Nipun Poddar