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Researchers have introduced a new generative modeling framework called the Idempotent Generative Network. The framework utilizes three objectives: a reconstruction loss, an idempotent objective, and a tightness objective. The key idea is to train a neural network to be idempotent, meaning that the network can be applied sequentially without changing the result beyond the initial. The approach is based on training a model to be idempotent, which allows for better generative modeling. The paper proposing this approach was authored by Shocher, Dravid, Gandelsman, Mosseri, Rubinstein, and Efros from UC Berkeley and Google Research.
Idempotent Generative Network Shocher et al.: https://t.co/K7dLmRzVUz #ArtificialIntelligence #DeepLearning #MachineLearning https://t.co/3mdItOs2q5
[CV] Idempotent Generative Network A Shocher, A Dravid, Y Gandelsman, I Mosseri, M Rubinstein, A A. Efros [UC Berkeley & Google Research] (2023) https://t.co/hVSKyEkCM7 - The paper proposes a new generative modeling approach based on training a model to be idempotent, meaning… https://t.co/brSCPCK63N https://t.co/hNEHMoxtAJ
Idempotent Generative Network paper page: https://t.co/gxtvzrgruq propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changing the result beyond the initial… https://t.co/hFeLJmloGb https://t.co/103cuEmvfe
Time-series Generation by Contrastive Imitation. https://t.co/DMOgf6g9Ym
Idempotent Generative Network project page: https://t.co/NWNtKTkLFo abs: https://t.co/49CaPSrMo2 Introduces a new generative modeling framework that utilizes three objectives: a reconstruction loss, an idempotent objective, and a tightness objective. The key idea is that for… https://t.co/XETprzA01T https://t.co/0Uvf9zmuKW