From 9120b3ffc17bcc3ceaef4a14683e6d1a740f00c8 Mon Sep 17 00:00:00 2001 From: Gayoon Choi Date: Mon, 21 Oct 2024 16:58:02 +0900 Subject: [PATCH] change images --- promptdistill2024/index.html | 49 +++++++----------- .../static/images_promptdistill/abl1.png | Bin 446114 -> 0 bytes .../static/images_promptdistill/abl2.png | Bin 507517 -> 0 bytes .../images_promptdistill/compression.png | Bin 95374 -> 0 bytes .../images_promptdistill/mainfigure.png | Bin 65566 -> 139018 bytes .../static/images_promptdistill/pipeline.png | Bin 148195 -> 161588 bytes .../static/images_promptdistill/tab4.png | Bin 0 -> 182794 bytes 7 files changed, 19 insertions(+), 30 deletions(-) delete mode 100644 promptdistill2024/static/images_promptdistill/abl1.png delete mode 100644 promptdistill2024/static/images_promptdistill/abl2.png delete mode 100644 promptdistill2024/static/images_promptdistill/compression.png create mode 100644 promptdistill2024/static/images_promptdistill/tab4.png diff --git a/promptdistill2024/index.html b/promptdistill2024/index.html index 9635725..83cfe0f 100644 --- a/promptdistill2024/index.html +++ b/promptdistill2024/index.html @@ -883,9 +883,8 @@

Prompt Distillation for Weight-free Tra - + --> @@ -942,13 +938,14 @@

Abstract

- While Vision Transformer has facilitated remarkable advancements in computer vision, it concurrently requires vast amounts of training data and iterations. - Transfer learning utilizes knowledge from pre-trained networks to overcome these, but it is difficult to share entire network weights in the medical field where data privacy must be taken into account. - We introduce the innovative transfer strategy, called \textit{Prompt Distillation}, which shares prompts instead of network weights. - Prompt distillation compresses knowledge of the pre-trained network into prompts by effectively leveraging the attention mechanism. - In experiments, it outperformed training from scratch and achieved close performance to full weights transfer learning, while reducing the parameter scale up to 90 times lighter than full weights. + While Vision Transformers have facilitated remarkable advancements in computer vision, they require vast training data and iterations. + Transfer learning is widely used to overcome these challenges, utilizing knowledge from pre-trained networks. + However, sharing entire network weights for transfer learning is difficult in the medical field due to data privacy concerns. + To address these concerns, we introduce an innovative transfer strategy called Prompt Distillation which shares prompts instead of network weights. + It compresses knowledge in pre-trained networks to prompts by effectively leveraging the attention mechanism. + In experiments, it outperformed training from scratch and achieved comparable performance to full-weight transfer learning, while reducing the parameter scale by up to 90 times compared to full-weight. Moreover, it demonstrates the ability to transfer knowledge between already-trained networks by merely inserting prompts. - Its applications were validated through medical image classification across three domains, chest X-ray, pathology, and retinography, distinct in degrees of the distribution shift. + It is validated through medical image classification across three domains, chest X-ray, pathology, and retinography, distinct in degrees of distribution shifts.
@@ -981,13 +978,9 @@

Pipeline


The pipeline of Prompt Distillation based transfer learning. - In the pretraining step, a network is trained with a large-scale dataset and acquires generalization. - In the prompt distillation step, we inject prompts in the pre-trained network's embedding space and compress knowledge from the pre-trained networks into prompts. - Compression is conducted by training the network in supervision for simplicity, and nested dropout and knowledge distillation techniques can be applied. - Prompt distillation is divided into two categories based on where prompts are projected. - Prompt distillation projects prompts onto query, key, and value vectors, and updates prompts to adapt the network toward a deep understanding of training data. - Learned prompts are shared instead of pretrained network weights. - In a transfer learning step, targets with a smaller, task-specific dataset attach learned prompts to their networks and leverage generalized knowledge in prompts to optimize particular objectives. + After pre-training, prompts are inserted inside the pre-trained source network and trained for a few epochs (Step 2). + Then, these learned prompts are shared in place of network weights to target networks for transfer learning (Step 3). + ``Train" and ``Frozen" refer to whether backpropagation is performed, which involves calculating the gradients and updating the parameters, or not.

@@ -996,8 +989,10 @@

Knowledge Compression Strategies

Main figure

- An illustration of the knowledge compression for distilling prompts. - Visual Prompt Tuning injects prompts between a class embeddings and patch embeddings and learns relationship through supervised learning. + The framework of prompt distillation. + Red tokens represent prompts, which are injected into Transformer encoders. + During prompt distillation, Transformer remains frozen (i.e. not back-propagated), and only prompts are trained (i.e. back-propagated). + Prompts from the previous layer are removed as new prompts are inserted into the next layer.

@@ -1056,15 +1051,9 @@

Quantitative Ablation


The Number of Prompt and Distillation Epochs

- transfer learning table -

- Effects of the number of prompt embeddings and distillation epochs on prompt projection types. -

-
-
- knowledge compression table + transfer learning table

- CEffects of the number of prompt embeddings and distillation epochs on prompt compression strategies. + Effects of the number of prompt embeddings and distillation epochs.

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