Computer vision system marries image recognition and generat

A new framework known as MAGE merges the two key tasks of image generation and recognition, typically trained separately, into a single cohesive system. The work was developed by researchers from MIT and Google.

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Tianhong Li , Huiwen Chang , Dina Katabi , Shlok Kumar Mishra , Zhan Zhang , Dilip Krishnan , Huisheng Wang , Department Of Electrical Engineering , University Of Maryland Ph , Artificial Intelligence Laboratory , Google Research , Watson Research Collaboration , Google , Masked Generative Encoder , Machine Intelligence , Nicole Pham Professor , Electrical Engineering , Computer Science , Maryland Phd , Google Cloud Platform , Computer Vision , Mit Csail , It Ibm Watson Research Collaboration , It Ai Research , Asked Generative Encoder Mage , Image Generation , Image Recognition , Image Synthesis , Generative Modeling , Representation Learning , Emantic Tokens , Asked Token Modeling , Imagenet ,