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[1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text. T5 models are usually pretrained on a massive dataset of text and code, after which they can perform the text-based tasks that are similar to their pretrained tasks.
Multimodal models can either be trained from scratch, or by finetuning. A 2022 study found that Transformers pretrained only on natural language can be finetuned on only 0.03% of parameters and become competitive with LSTMs on a variety of logical and visual tasks, demonstrating transfer learning. [102]
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]
It was superseded by the GPT-3 and GPT-4 models, which are no longer open source. GPT-2 has, like its predecessor GPT-1 and its successors GPT-3 and GPT-4, a generative pre-trained transformer architecture, implementing a deep neural network , specifically a transformer model, [ 6 ] which uses attention instead of older recurrence- and ...
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related task. [1] For example, for image classification, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
Flamingo demonstrated the effectiveness of the tokenization method, finetuning a pair of pretrained language model and image encoder to perform better on visual question answering than models trained from scratch. [84] Google PaLM model was fine-tuned into a multimodal model PaLM-E using the tokenization method, and applied to robotic control. [85]
The Stable Diffusion model supports the ability to generate new images from scratch through the use of a text prompt describing elements to be included or omitted from the output. [8] Existing images can be re-drawn by the model to incorporate new elements described by a text prompt (a process known as "guided image synthesis" [ 49 ] ) through ...
Scratch was developed based on ongoing interaction with youth and staff at Computer Clubhouses. The use of Scratch at Computer Clubhouses served as a model for other after-school centers demonstrating how informal learning settings can support the development of technological fluency. [55] Scratch 2.0 was released on 9 May 2013. [14]