in this case, the cloned networks are the U-Net encoder/middle layers, and the results of the each trainable copy are fed in each middle/decoder block.
MACHINE LEARNING
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Convolutional Architecture Encodes Condition Image in Cloned U-Net
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it is worth mentioning that the authors used a convolutional architecture to encode the condition image before feeding it within the cloned U-Net.
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Zero Convolutions Training: Sudden Convergence Phenomenon Explained
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the use of zero convolutions while training results in a funny behavior that the authors name “sudden convergence phenomenon”, where the model is suddenly able to follow the input conditions, as depicted in the following image:
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ControlNet Architecture Clones Diffusion Model Weights
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the following is a general representation of ControlNet's architecture. first, it clones the weights of a diffusion model. Then, it trains the cloned weights to control the original model with the task from the input condition.
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Zero Convolutions Enable Progressive Control Learning in AI Models
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the goal of this architecture is to keep as much as possible all the knowledge learned by the original model. the trainable network learns how to perform the control in a progressive way thanks to the use of zero convolutions.
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Zero Convolutions in ControlNet: Progressive Training Influence
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zero convolutions are 1D convolutions with weights and biases initialized to 0s. note how at the beginning of the training ControlNet will not affect the original network at all, but as it gets trained it will progressively start influencing the generation with the condition.
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ControlNet: Conditioning Diffusion Models on Arbitrary Input Features
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ControlNet is a method that can be used to condition diffusion models on arbitrary input features, such as image edges, segmentation maps, or human poses.
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AI Model Interprets Show More Less Signals for Better Feed Content
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We trained an AI model to effectively interpret intent behind “Show More” & “Show Less” signals to surface more of the content people want to see in their Feeds — even if they rarely interact with the signals. More on how we built it https://
bit.ly/3Edj08k -
Language Models Semantics NLP Real-world Applications Discussion
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Prof. Edward Grefenstette @egrefen discusses language models, semantics, philosophy, and more, diving into how NLP can solve real-world problems in this exciting conversation with @MLStreetTalk
: https://
youtu.be/i9VPPmQn9HQ?ut
m_source=twitter&utm_medium=social
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FRMT: Few-Shot Region-Aware Machine Translation Benchmark
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Presenting FRMT, a new dataset and evaluation benchmark for Few-Shot Region-Aware Machine Translation that seeks to drive research progress on equitably serving speakers of different language varieties. Learn more and see how current models fare → https://t.co/AUlQMPQIz7 pic.twitter.com/fKgymh9d6t
— Google AI (@GoogleAI) 17 février 2023Presenting FRMT, a new dataset and evaluation benchmark for Few-Shot Region-Aware Machine Translation that seeks to drive research progress on equitably serving speakers of different language varieties. Learn more and see how current models fare → https://
goo.gle/3IvnPwc