MAE-VQGAN

Choosing the Right Loss Function in PyTorch: MAE, MSE, Huber

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Choosing the Right Loss Function in PyTorch: MAE, MSE, Huber

Author: Little Cola Demon King @ Zhihu (Authorized) Source: https://zhuanlan.zhihu.com/p/378822530 Editor: Jishi Platform This article summarizes how to choose the appropriate loss function for different application scenarios, compares the advantages and disadvantages of different loss functions, and provides relevant PyTorch code. Direct Results: Image excerpted from the end of this article Main Text: In both … Read more

Impact of Transformer Model Size on Training Objectives

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Impact of Transformer Model Size on Training Objectives

Click the above“Beginner Learning Vision” to select “Star” or “Pin” Valuable Insights Delivered First-Hand Source: PaperWeekly Editor: Jishi Platform Jishi Guide Is there a close relationship between the configuration of Transformers and their training objectives? This article aims to introduce work from ICML 2023: Paper Link: https://arxiv.org/abs/2205.10505 01 TL;DR This paper studies the relationship between … Read more

Visual Prompt Engineering: No Fine-Tuning Required

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Visual Prompt Engineering: No Fine-Tuning Required

↑ ClickBlue Text Follow the Jishi platform Author丨Tech Beast Editor丨Jishi Platform Jishi Guide How to adapt a pre-trained visual model to new downstream tasks without specific task fine-tuning or any model modifications? >> Join the Jishi CV technology exchange group and stay at the forefront of computer vision Table of Contents 1 Completing Visual Prompting … Read more