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This repository is the official implementation of Prompt Generation Networks for Efficient Adaptation of Frozen Vision Transformers by Jochem Loedeman, Maarten Stol, Tengda Han and Yuki M Asano.
In this work, we propose the Prompt Genera-tion Network (PGN) that generates high performing, input-dependent prompts by sampling from an end-to-end learned library of tokens. We further introduce the “prompt inver-sion” trick, with which PGNs can be efficiently trained in a latent space but deployed as strictly input-only prompts for inference.
12 de oct. de 2022 · To address this, we propose the Prompt Generation Network (PGN) that generates input-dependent prompts by sampling from a learned library of tokens. We show the PGN is effective in adapting pretrained models to various new datasets.
12 de oct. de 2022 · In this work, we propose the Prompt Generation Network (PGN) that generates high performing, input-dependent prompts by sampling from an end-to-end learned library of tokens. We further introduce the "prompt inversion" trick, with which PGNs can be efficiently trained in a latent space but deployed as strictly input-only prompts for ...
14 de abr. de 2017 · Get To The Point: Summarization with Pointer-Generator Networks. Neural sequence-to-sequence models have provided a viable new approach for abstractive text summarization (meaning they are not restricted to simply selecting and rearranging passages from the original text).
22 de sept. de 2022 · To address this, we propose the Prompt Generation Network (PGN) that generates input-dependent prompts by sampling from a learned library of tokens. We show the PGN is effective in adapting pretrained models to various new datasets.
17 de sept. de 1994 · The Julius Baer Generation Cup takes place on chess24 18th to 25th September 2022. Play starts at 17:00 BST each day. The event is part of the Meltwater Champions Chess Tour. The field has a deliberate mix of generations.
