Dcgan jobs
I am looking for a skilled ML developer who can implement a GAN model for my academic research project. The specific GAN model I am looking to implement is a GAN for chest X-rays, with a focus on overcoming class imbalance and data augmentation. Skills and experience required: - Strong understanding and experience with GAN models, specifically DCGAN, WGAN, or CGAN - Experience working with medical imaging datasets, particularly chest X-rays - Proficiency in data preprocessing techniques to handle class imbalance and generate synthetic data - Familiarity with machine learning frameworks such as TensorFlow or PyTorch - Knowledge of evaluation metrics for GAN models in the medical domain The project will involve using an existing dataset for training the GAN model, which I already ha...
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 am looking for an experienced freelancer to help me with a Machine Learning project, specifically related to image inpainting problems using a DCGAN. The goal is to make an AI model that can complete objects in images that have been partially erased or are incomplete. The desired output resolution is low, under 512x512. I don't have any specific datasets for this project, so the freelancer will have the freedom to choose which datasets to use. Regarding documentation, I already have code for the project, but need to train the model and send the results. Thank you very much in advance!
I have a paper that proposed a hybrid framework, I want the code of "a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images". It was based on DCGAN but they have some differences in the architecture. The project requests important points: 1- Write the code in Python programing language using Pytorch in Google Colaboratary 2- Explain each build layer and the details of this layer; how and why (the purpose of changing it) 3- Write explain comment for every function 4- Explain how can dwnload the images are generated from the model on my drive 5- How i can use the same code with different dataset with different size such as: 512*512 640*640 1536*2048 6- Describes the steps to run the project 7- Firstly used the same dataset in the paper (it is ...
I am looking for a sk...can help me create a GANs model for image generation. I will be providing the dataset, so no need to worry about that. The ideal candidate should have experience with GANs and deep learning in TensorFlow. The generated images should be of low resolution, less than 512x512. If you have experience with image generation using GANs in TensorFlow, I'd love to hear from you. I require three models: - DCGAN model and code (Convolution based) - Cycle GAN model and code (Convolution based) - Vision transformer GANs model and code For all three evaluation table and FID (Fréchet Inception Distance) for image quality evaluation Dataset: You can ask me for any missing details in your bid I'll update it.
1. Google Colab code 2. Report of the results 3. Deadline 20 days after selection 4. After selection, you must send progress to avoid mistakes within 5days. 5. If it is not work in Google Colab on my Mac, you must fix it through TeamViewer or so. I. Music Dataset CSV data (fixed, I will give you and ...fix it through TeamViewer or so. I. Music Dataset CSV data (fixed, I will give you and discuss.) 2. Data analysis: (Reading and Understanding the Data, Showing raw data, Visualizing Audio Files, Plot Raw Wave Files, Spectrograms, pectral Rolloff, Spectral Centroid, MFCC (Mel-Frequency-Cepstral Coefficients), Chroma Feature, Zero Crossing Rate, Feature Extraction, Scaling the Features) 3. Build DCGAN (Deep Convolutional Generative Adversarial Network) for emotion-based genre classi...
Music Genre Classification with DCGAN Work content 1. Google Colab code 2. Report (Simple) the results 3. Deadline (10days after being selected or can be decided through talking. Should mention progress after 3-days). I. Dataset (music data) CSV data (fixed, I will give you or we can discuss) 2. Data analysis: (Reading and Understanding the Data, Showing raw data, Visualizing Audio Files, Plot Raw Wave Files, Spectrograms, pectral Rolloff, Spectral Centroid, MFCC (Mel-Frequency-Cepstral Coefficients), Chroma Feature, Zero Crossing Rate, Feature Extraction, Scaling the Features) 2. Build GRU (Gated Recurrent Unit) for genre classification 3. Model Evaluation of DCGAN for genre classification (Confusion matrix, data results, figures) 4. Comparison with CNN results (or others...
Time Sensitive: 4/14 11:59 PST Code a DCGAN and/or proGAN model that can take a random noise image or feature template image and generate images of dark skin conditions (Melanoma) using Keras (preferably in Google Colab too) Pricing: $150 (negotiable) Attached is the Melanoma Images (some graphic) & my current Pix2Pix GAN If you have any questions, please contact me!
...Inception Distance (FID) between 1000 generated and real data as the metric for evaluation. 3 Models for Assignment 3.1 Bayes’s Classifier with several Class Conditional Densities such as Gaussian, GMMs (Have to code up EM) 3.2 Bayes’s Classifier with different density estimates (ML, MAP and Parzen Window and nearest neighbor estimates) 5 Also using any one data set do the following:- 5.1 Train a DCGAN for one of the datasets given. Compute FID and plot the generated images in a 10×10 grids. 5.2 Train a VAE on the same dataset and repeat the above experiment. 5.3 Plot t-SNE embeddings for both 2 and 3 dimensions for one of the datasets. 5.4 Compare the above plots with the ones obtained using PCA on the same dataset on 2 and 3 dimens...
...Inception Distance (FID) between 1000 generated and real data as the metric for evaluation. 3 Models for Assignment 3.1 Bayes’s Classifier with several Class Conditional Densities such as Gaussian, GMMs (Have to code up EM) 3.2 Bayes’s Classifier with different density estimates (ML, MAP and Parzen Window and nearest neighbor estimates) 5 Also using any one data set do the following:- 5.1 Train a DCGAN for one of the datasets given. Compute FID and plot the generated images in a 10×10 grids. 5.2 Train a VAE on the same dataset and repeat the above experiment. 5.3 Plot t-SNE embeddings for both 2 and 3 dimensions for one of the datasets. 5.4 Compare the above plots with the ones obtained using PCA on the same dataset on 2 and 3 dimens...
Image processing , DCGAN, Convolution neural networks it's CNN based image recognition project, using DCGAN,,,,As per the document, the task is to generate images using DCGAN and CNN. Model is not mentioned in the document. If you allow me to use pre built model then I can complete the task within 45 hours.,,,, Pls if you can do it start your bid with CNN >>
I needed a developer who knows image processing. About my project- This is a image processing project as already mentioned, you need to create a python project in which we are able to convert sketch of images into real colourful images. Performance evaluation including: l2 norm Ssim Confusion matrix
Hello dear freelancers, 1) Train machine learning classifier on the following kaggle dataset: dataset: 2) Train 3 types of GANS to generate the EEG data and train a machine learning classifier 3) Analyse the improvements in accuracy of each classifier on the same dataset using the GAN-pretrained classifier Deliverables: - Train classifier and record performance accuracy (Can follow notebooks on Kaggle). - Train DCGAN, WGAN and SGAN to generate EEG data and record kl-divergence - Train classifier on augmented dataset (real+training), and dataset of just generated data, and record accuracy of classifier Bugdet is flexible, looking forward to work with someone in long-term
Hello dear freelancers, 1) Train machine learning classifier on the following kaggle dataset: dataset: 2) Train 3 types of GANS to generate the EEG data and train a machine learning classifier 3) Analyse the improvements in accuracy of each classifier on the same dataset using the GAN-pretrained classifier Deliverables: - Train classifier and record performance accuracy (Can follow notebooks on Kaggle). - Train DCGAN, WGAN and SGAN to generate EEG data and record kl-divergence - Train classifier on augmented dataset (real+training), and dataset of just generated data, and record accuracy of classifier Bugdet is flexible, looking forward to work with someone in long-term
to build 2 or 3 GANs architectures to generate art. ADDITIONAL INFORMATION DCGAN/WGAN/progessive GAN/CCGAN/BigGAN architectures or any other. Comparing the models to select one best this is a project based on generative adversarial Networks. can you build 3 models for generating art? and compare them and choose one best of it
to build 2 or 3 GANs architectures to generate art. ADDITIONAL INFORMATION DCGAN/WGAN/progessive GAN/CCGAN/BigGAN architectures or any other. Comparing the models to select one best this is a project based on generative adversarial Networks. can you build 3 models for generating art? and compare them and choose one best of it
to build 2 or 3 GANs architectures to generate art. ADDITIONAL INFORMATION DCGAN/WGAN/progessive GAN/CCGAN/BigGAN architectures or any other. Comparing the models to select one best this is a project based on generative adversarial Networks. can you build 3 models for generating art? and compare them and choose one best of it
to build 2 or 3 GANs architectures to generate art. ADDITIONAL INFORMATION Needs to be completed in less than 10 days DCGAN/WGAN/progessive GAN/CCGAN/BigGAN architectures or any other. Comparing the models to select one best
to build 2 or 3 GANs architectures to generate art. ADDITIONAL INFORMATION Needs to be completed in less than 10 days DCGAN/WGAN/progessive GAN/CCGAN/BigGAN architectures or any other. Comparing the models to select one best
1. python notebook to resize, rotate, flip, crop and otherwise manipulate images to increase the number of images in a dataset available for training in machine learning. the inputs to be taken from google drive and the outputs to be saved to new google drive directories 2. repair to my old DCGAN model which used to run in tf 1 but no longer works and needs to be repaired to run preferably in tf2
I am an artist using machine learning to make images for paintings. I need to make a new DCGAN in a python notebook to run in Google colab. I would like to base it on this tensorflow DCGAN tutorial example to run on tensorflow 2.3.0: with the following modifications: - a process to resize training images (take training images from user specified google drive and resize them for the model) - output images to be user specified at 64 x 64 x 3, or 128 x 3 x 3: (the example is 28 x 28 x 1) - output images to save to a user specified google drive folder at specified intervals (eg, every 100 epochs) I can share with you my earlier model which had those features and I was happy with, but has stopped working (I think due to
I would like to use a dcgan to generate images for an art project. Using a relatively small dataset of 200 or so training images I'd like to generate new images. I have been using a DCGAN to produce results but recently the algorithm has stopped working for an unknown reason. This might be possible to fix or it might be necessary to build a new GAN. Examples of images produced in the previous project are attached - these were trained on a dataset of only 50 or so images. This was run as a python notebook in Google Colab. I need to be able to easily change the training images.
I have a DCGAN model which has been working well, but now it is only producing noise and not learning. The discriminator accuracy is going to 100% after a small number of iterations. Something has changed and I can't work out what.
Hi, I want to prepare a presentation about gan. I want to have examples of basic gan algorithms, and visualize the generator's network output using the published pre-trained model. Open sources would be fine. 10-15 page presentation will be enough. One of the algorithms has to be first published one which is proposed in the ian Goodfellow paper. DCGAN algorithm examples are also required. The third one is not yet determined but it will be the one that has repo in GitHub. Are you interested?
Write a python code that will use DCGAN from keras and generate mnist digit images.
About 2 years ago I had someone write some DCGAN code for me. It worked well but now that tensorflow is upgraded there are some problems that I would like to get fixed.
We are looking for someone who has meaningful experience in pose detection. By learning the relationship between pose and real-world video footage, we want to be able to transfer motion between two people in a realistic way. We are trying to create a product that woul...video footage, we want to be able to transfer motion between two people in a realistic way. We are trying to create a product that would allow two distinct motions to be detected, compared and corrected. If interested, we will review the brief with you and request a full proposal and estimate. Pose Estimation & proven record of problem-solving. Deep Convolutional Generative Adversarial Network (DCGAN) expertise a plus. Please send example of previous work in your application. Interview process will consist of...
We are looking for someone who has meaningful experience in pose detection. By learning the relationship between pose and real-world video footage, we want to be able to transfer motion between two people in a realistic way. We are trying to create a product that woul...video footage, we want to be able to transfer motion between two people in a realistic way. We are trying to create a product that would allow two distinct motions to be detected, compared and corrected. If interested, we will review the brief with you and request a full proposal and estimate. Pose Estimation & proven record of problem-solving. Deep Convolutional Generative Adversarial Network (DCGAN) expertise a plus. Please send example of previous work in your application. Interview process will consist of...
I want to create new computer generated images with GAN/DCGAN but I have zero code/AI knowledge. So I need you to setup the GAN or DCGAN (whatever works best for my purpose) so I just have to define the dataset aka images and then can generate new images. Experience in GAN/DCGAN recommended
...adversarial network to train on the CelebA Dataset and learn to generate face images. The top-level notebook () will guide you through the steps you need to take to implement and train a GAN. You will train two different models, the original GAN and LSGAN, which has a different loss function. The generator and discriminator network architectures you will implement are roughly based on DCGAN. We also provide with a notebook to help with debugging called GAN_debugging.ipynb. This notebook provides a small network you can use to train on MNIST. The small network trains very quickly so you can use it to verify that your loss functions and training code are correct. You will need to use a GPU for training your GAN. We recommend using Colab to debug, but a Google Cloud machine once y...
I have trained a Pytorch DCGAN-model to a sufficient level, and need to generate new samples. But there is no included generator amongst the examples and I’m a novice and cant write one myself.
