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Image Generation with Probabilistic Diffusion Models

Introduction:

1. DDPM - Diffusion Models Beat GANs on Image Synthesis (Machine Learning Research Paper Explained)

2. Lil Log What are Diffusion Models?

Papers:

[1]Denoising Diffusion Probabilistic Models

[2] DENOISING DIFFUSION IMPLICIT MODELS

[3]On Fast Sampling of Diffusion Probabilistic Models

Problem 1: what is the problem the two papers aim to solve, and why is this problem important or interesting? (5 points)

Problem 2: 1) summarize the three methods, including high-level ideas as well as technical details: the relevant details that are important to focus on (e.g., if there’s a model, define it; if there is a theorem, state it and explain why it’s important, etc) 2) what are the major differences of the three methods? (15 points)

Problem 3: implement DDPM [1] and test it on MNIST dataset. You need to generate samples and perform an interpolation experiment with your trained model. (30 points)

Reference code: [login to view URL]

Problem 4: implement DDIM [2] and test it on MNIST dataset. You need to generate samples and perform an interpolation experiment with your trained model. (30 points)

Reference code: [login to view URL]

Problem 5: implement FastDPM [3] and test it on MNIST dataset. You need to generate samples and perform an interpolation experiment with your trained model. (30 points)

Reference code: [login to view URL]

Problem 6: visualize the denoising process on CIFAR-10 and CelebA-HQ datasets for all three methods, respectively (e.g., Figure 6 of [1]). You are allowed to use the pre-trained models provided by the authors. (20 points)

Problem 7: interpolate source images on CIFAR-10/100 and CelebA-HQ datasets with all three methods, respectively (e.g., Figure 8 of [1]). You are allowed to use the pre-trained models provided by the authors. (20 points)

Problem 8 (bonus): is it possible to use diffusion models to generate text data? If you think that it is impossible or tough, explain why; If you think that it is possible, you will get bonus credits by realizing the idea. (80 points)

Kemahiran: Python, Machine Learning (ML), Pytorch, Keras, Tensorflow

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Tentang Majikan:
( 0 ulasan ) Binghamton, United States

ID Projek: #32234437

5 pekerja bebas membida secara purata ₹14500 untuk pekerjaan ini

suyashdhoot

Hi I am a very experienced statistician, data scientist and academic writer. I have completed several PhD level thesis projects involving advanced statistical analysis of data. I have worked with data from several comp Lagi

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shashaev96

Master in Machine Learning and Software Developing️ Hello , I hope you are safe and Doing well I have seen your project requirements , I am looking to discuss further with you Hope we will meet soon to discuss further Lagi

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rexzetsolutions

Yes, i can implement Image Generation with Probabilistic Diffusion Models, message me i am ready to start work from right now.

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starktynt

Hi , I have read your requirements , i can provide you with the Solutions and implementation you are looking for , I have many years of experience with data analysis , python , ml , nlp etc , Would love to discuss thi Lagi

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Rgoski329

I am pursuing M.Sc. (Data Science and Big Data Analytics) I have worked on a project where we analyzed U.S. Coal Production Prediction and I have hands-on experience in Tableau which helps in Data Viz and i also took p Lagi

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