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Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.
The Latent Diffusion Model (LDM) [1] is a diffusion model architecture developed by the CompVis (Computer Vision & Learning) [2] group at LMU Munich. [3]Introduced in 2015, diffusion models (DMs) are trained with the objective of removing successive applications of noise (commonly Gaussian) on training images.
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
Neural style transfer (NST) refers to a class of software algorithms that manipulate digital images, or videos, in order to adopt the appearance or visual style of another image. NST algorithms are characterized by their use of deep neural networks for the sake of image transformation.
Instead, one removes the task head and replaces it with a newly initialized module suited for the task, and finetune the new module. The latent vector representation of the model is directly fed into this new module, allowing for sample-efficient transfer learning. [1] [8] Encoder-only attention is all-to-all.
Each generated image starts as a constant [note 1] array, and repeatedly passed through style blocks. Each style block applies a "style latent vector" via affine transform ("adaptive instance normalization"), similar to how neural style transfer uses Gramian matrix. It then adds noise, and normalize (subtract the mean, then divide by the variance).
GPT-3-style language model Megatron-Turing NLG: October 2021 [28] Microsoft and Nvidia: 530 [29] 338.6 billion tokens [29] 38000 [30] Restricted web access Trained for 3 months on over 2000 A100 GPUs on the NVIDIA Selene Supercomputer, for over 3 million GPU-hours. [30] Ernie 3.0 Titan: December 2021: Baidu: 260 [31] 4 Tb Proprietary Chinese ...
Since the transformer architecture enabled massive parallelization, GPT models could be trained on larger corpora than previous NLP (natural language processing) models.. While the GPT-1 model demonstrated that the approach was viable, GPT-2 would further explore the emergent properties of networks trained on extremely large corpo