Cyclegan trabalhos

I am developing a Master's thesis project in urban design that leverages artificial intelligence to explore how urban layouts respond to the insertion of new projects. The goal is to build a generative deep learning model using GANs (Pix2Pix or CycleGAN preferred) to generate a new urban pattern in response to a proposed project, such as a square, park, or building, placed within an existing urban fabric. But beyond generation, the tool must also analyze and evaluate the spatial impact of the generated pattern — measuring how the project affects the structure, density, connectivity, and overall behavior of the surrounding fabric. The model must not simply attach the new project into the layout but learn to integrate it in a way that reshapes and reorganizes the surroun...

I am seeking a skilled freelancer to run and slightly modify three algorithms using Python. The task involves working with a zip folder containing 200 images that need to be processed through a Stable Diffusion algorithm and a CycleGAN. The goal is to optimize these algorithms by adjusting parameters such as epoch size and adding bar charts to visualize key metrics like loss, accuracy, precision, recall, and FID. ** Algorithms are working and ready to be used ** Key Requirements: - Run and modify Stable Diffusion and CycleGAN algorithms multiple times for optimization. - Integrate charts to track and visualize metrics: accuracy, loss, precision, recall, and FID ( will be discussed ) - Generate datasets from both algorithms and subsequently process them using the YOLO-v8 alg...

Question: NaN Error During Gradient Calculation in CycleGAN Training; How to solve this Description: Hello, I am a master’s student in the Department of Electrical and Electronic Engineering, working on AI training for image correction (image-to-image transformation) using Python TensorFlow. My current setup involves training a CNN with CycleGAN. I am encountering an issue during training. Specifically: Training setup: 1,500 images, learning rate 2e-4, up to 20 epochs. The issue: During training, the first NaN error occurs when calculating gradients of forward generator and inverse generator both, at epoch 9. This makes others NaN also and training fails. When I set the learning rate to 2e-5, which is smaller, but the error also occurs around epoch 162. What I&rs...

...and experience with transformer architectures, particularly for time series and geospatial data. Ability to adapt transformers for various use cases such as sequence modeling and attention mechanisms. Generative Adversarial Networks (GANs): Expertise in designing and training GANs, especially for applications in geospatial data synthesis and climate modeling. Familiarity with conditional GANs, CycleGAN, and other GAN variants. Convolutional Neural Networks (CNNs): Strong background in developing CNNs for image processing, segmentation, and geospatial data analysis. Experience in optimizing CNNs for performance on large datasets. Graph Neural Networks (GNNs): Proficient in the design and implementation of GNNs for spatial data analysis and modeling relationships between entitie...

I am looking for a skilled freelancer who can generate synthetic text data using GAN for the purpose of training a model. The ideal candidate should have experience in working with GANs and should be familiar with different architectures such as DCGAN and CycleGAN. However, since I have no preference for a specific architecture, the freelancer can choose the most suitable one for the task. The main goal of this project is to generate high-quality synthetic text data that can be used for training a model.

I'm looking for a Freelancer to to provide a comprehensive explanation of a CycleGAN code in Python. The explanation should be in the form of a video tutorial or zoom meeting that thoroughly explains the full code and leaves nothing to interpretation. I need someone to truly break down and explain everything in great detail and depth so that the code would make sense to any beginner programmer. If you think you have the skillset and knowledge to provide such a comprehensive explanation, please do apply. Thank you.

Dear Freelancer, Please bid, if you are an expert in Tensorflow, Pytorch, keras and NLP, This project is about author style transfter using GANs such as WGAN, cycleGan etc.

Using cycleGAN method via python to clean receipt and document of blur shadow and pen writing. Must develop notebook for local laptop usage. Must guide and explain code to me for source input and model minor modification

CycleGAN works on images (translates an image from one domain to another domain). I want you to create a python script GAN similar to CycleGAN but works on strings of text instead of images. Requirements: The goal is to have a neural network GAN architecture that can learn how to transform each row in the list of lines from automatically to any text style file provided, for example 1. (should learn doubling letters) 2. (should learn adding the characters 'xyz' at end of string) 3. (should learn toggling case). The Neural Network python script you create should be adaptable to read and learn from any other Style text filename inputted from command line arguments. Each input line string is 300 characters. Python 3 + TensorFlow 2 Files: Attached

I'd like to train a CycleGAN or GAN-related model on a set of artworks and generate artworks similar to another domains (ex: Picasso style adapted to Rembrandt style). The script should be reproducible in R

I am looking for someone to produce a series of images using Neutral Style or Cyclegan. Please message if interested! :)

This is a simple task, there is a library on GitHub for generating a sketch from image and image from sketch ... I want a python program that uses the pre-trained model and converts an image to sketch and the vice versa. github library: pretrained model:

This is a simple task, there is a library on GitHub for generating a sketch from image and image from sketch ... I want a python program that uses the pre-trained model and converts an image to sketch and the vice versa. github library: pretrained model:

I require a programer expert enough in the Wolfram Language (i.e., Mathematica and its Neural Net Framework) an... These translations will need to include coding custom layers, loss functions, regularization methods, and most significantly, means of training in Mathematica, corresponding to the relevant python source and/or academic reference paper in question. In addition to translating these four models, I also require means of training three models already translated and available architecturally (Cyclegan, pix2pix, SRGAN) in the Wolfram Language. Finally, I require three custom NetDecoders, two of which would correspond to NetEncoders that are already available (“Audio”, “AudioSTFT”). I will provide further details, example code, necessary referenc...

...verify it will improve with time. The network must be configurable to train and also to generate results, both final and intermediate. For it to function in a useful manner, I must be able to, later on, configure it to dump intermediate products, and also to be able to accept intermediate products as a command line input. The outcome I want is a novel trained model which achieves maximum possible compression for a specific data set (ascii text files) with the minimum symbols library necessary to reconstitute it, by virtue of having intuitively learned the mechanics required to compress text. A compression engine only achieves compression by saving bits overall. If the product of this engine is a model

i need a novel work on image to image translation tasks. it can be based on the existing work like cycleGAN, Multi-model unsupervised image to image translation(MUNIT), towards unsupervised image to image translation(unit), self-attention GANs, RelativisticGAN etc. but the work should be worthy enough for my master thesis. main tasks are: 1) multi-model image to image translation on benchmark datasets like edge2shoes, edge2handbages, day2night, arial2map, semantic labels 2 realstic photo 2) the model should be train on unsupervised fashion like for unpair data. 3) the result should be comparable to state of the art.