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{ "item_title" : "Deep Generative Models", "item_author" : [" Anirban Mukhopadhyay", "Ilkay Oksuz", "Sandy Engelhardt "], "item_description" : "This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore. DG4MICCAI 2022 accepted 12 papers from the 15 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/3/03/118/575/3031185757_b.jpg", "price_data" : { "retail_price" : "59.99", "online_price" : "59.99", "our_price" : "59.99", "club_price" : "59.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Deep Generative Models|Anirban Mukhopadhyay

Deep Generative Models : Second Miccai Workshop, Dgm4miccai 2022, Held in Conjunction with Miccai 2022, Singapore, September 22, 2022, Proceedings

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Overview

This book constitutes the refereed proceedings of the Second MICCAI Workshop on Deep Generative Models, DG4MICCAI 2022, held in conjunction with MICCAI 2022, in September 2022. The workshops took place in Singapore.
DG4MICCAI 2022 accepted 12 papers from the 15 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community.

This item is Non-Returnable

Details

  • ISBN-13: 9783031185755
  • ISBN-10: 3031185757
  • Publisher: Springer
  • Publish Date: October 2022
  • Dimensions: 9.21 x 6.14 x 0.3 inches
  • Shipping Weight: 0.45 pounds
  • Page Count: 127

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