This repository, Policy-Gradient-Noise-Mask, explores the application of policy gradient methods in reinforcement learning to develop noise masks for various tasks.
Overview
Policy gradient methods are a class of reinforcement learning algorithms that optimize the policy directly. By parameterizing the policy and updating its parameters in the direction that maximizes expected rewards, these methods are particularly effective in high-dimensional or continuous action spaces.
In this project, we leverage policy gradient techniques to create noise masks that can be applied in different domains, such as image processing or signal enhancement. The approach involves training a policy network to generate masks that, when applied to input data, improve the performance of a downstream task.
Repository Structure
README.md: This file provides an overview of the project.
resnet10t_gradientp_RIN_64_64_k13_s6.pth: Pre-trained model weights for a ResNet-10 architecture using the policy gradient noise mask approach.
resnet10t_gradientp_RIN_64_64_k13_s6_FT.pth: Fine-tuned version of the above model.
resnet50_gradientp_RIN_64_64_k13_s6.pth: Pre-trained model weights for a ResNet-50 architecture using the policy gradient noise mask approach.
resnet50_gradientp_RIN_64_64_k13_s6_FT.pth: Fine-tuned version of the above model.
Getting Started
To utilize the pre-trained models provided in this repository:
Load the pre-trained model weights into your project. Ensure that your environment is set up with the necessary dependencies to support the model architectures provided.
Usage
The pre-trained models can be integrated into your projects for tasks such as image classification or enhancement. Below is a general guideline on how to load and use the models:
python
1import torch
2from torchvision import models
34# Example for loading ResNet-50 model5model = models.resnet50()6model.load_state_dict(torch.load('path_to_model/resnet50_gradientp_RIN_64_64_k13_s6.pth'))7model.eval()89# Now you can use the model for inference or further fine-tuning
License
This project is licensed under the Apache-2.0 License. For more details, refer to the LICENSE file.
Acknowledgments
We acknowledge the contributions of the open-source community and the resources provided by platforms like Hugging Face and GitHub that facilitate collaborative development and sharing of machine learning models.
