Autoencoder jobs

autoencoder and GAN for image sequence and normalizing flow SR

Objective and Deliverables 1) Write a one page each review of each document in the Autoencoder (AE) and Variational Autoencoder (VAE) folder. 40 pages for VAE and 4 pages for AE. All contained in one pdf document. 2) Each page should be double column consisting of the following A) Check first if the problem applies for Natural Lanaguage not only for images. Mention this in the context if not. A) Describe the context of the research B) Objective of the research and short result report C) The loss function and description of the variables and symbols used. D) Conclusion on the outcome of the method E) Source code (pytorch or tensorflow) of the project 3) Deliverables include latex pdf, latex source and of the original reference of all 44 papers in the folder. Ple...

Hi I have already built code for using convolutional autoencoder to reconstruct fingerprint images and it is working. I am Looking to add a function to verify or identify context using the appropriate databases and performance measures to compare fingerprint matching between the reconstructed images and the test images Best

I need a few Python tasks done within either today or tomorrow on Jupyter Notebook: 1. Use Multilayer Perceptron (MLP) deep learning models to estimate stats of a dataset. 2. Use Autoencoder (with CNN) deep learning models to remove noise from low resolution photographs. 3. Use Recurrent Neural Network (with LSTM or GRU) deep learning model to forecast the prices of a dataset The best models for the above three tasks should be reported along with the experiments done to get the best model. Should be supported with visualization of the data. Will provide more info to the correct candidate.

(few hours work 2-3 ) - topics like: Autoencoder greedy layer wise training Multi-layer perceptron SOM neural network etc Please bid if you good into it. Will share samples when we discuss individually

have an autoencoder model in PYTORCH with shape issues - need it resolved

Take a Variational Autoencoder, and replace the convolutional layers by RNN layers (Version 0) and by GRU layers (Version 1). Have 2 layers in the encoder and 2 layers in the decoder part. Use a donw sampling factor of 8 after the encoder. Train it on music and speech files , and use a separate set for testing. Then evaluate the audio quality after decoding

• Complete the code for 1D CNN Variational autoencoder (1D-CNN VAE) using a notebook as seen in VAE_pytorch_custom notebook in the attached. • Write and comment the meaning of the input of a 1D CNN and others used in pytorch and use the MNIST dataset for it. • Plot the 2D latent space generated by training a 1D CNN VAE and ensure the latent space corresponds to that obtained for 1D CNN VAE of tensorflow (see attached). • The first notebook is done for tensorflow and you can use ideas of the network structure for tensorflow to design your pytorch version.

• Complete the code for 1D CNN Variational autoencoder (1D-CNN VAE) using a notebook as seen in VAE_pytorch_custom notebook in the attached. • Write and comment the meaning of the input of a 1D CNN and others used in pytorch and use the MNIST dataset for it. • Plot the 2D latent space generated by training a 1D CNN VAE and ensure the latent space corresponds to that obtained for 1D CNN VAE of tensorflow (see attached). • The first notebook is done for tensorflow and you can use ideas of the network structure for tensorflow to design your pytorch version.

This is an audio visual project tha...project is able to do attention detection and recognition of emotions in real time. It uses emotions to analyze a person's level of attention and speech detection I need to improve the facial expression recognition system using an autoencoder, to make the system more robust and get a better recognition rate. There is a work that uses a convolutional autoencoder and novelty detection to improve recognition performance so that work is used to recognize new images, and I need to recognize new emotions There are some github codes that uses convolutional autoencoder for facial emotion recognition, and I whant to increment the ideia of novelty detection from the mentioned work, that just consist to use a different loss function th...

localize the forged area in a spliced forged image 1- code all comments and simple to comprehend 2- train on casia 2 dataset and test on casia 1 dataset on single image input-output casia 2 link casia 1 lin...in a spliced forged image 1- code all comments and simple to comprehend 2- train on casia 2 dataset and test on casia 1 dataset on single image input-output casia 2 link casia 1 link python or Jupyter notebook is accepted 3- use any techniques deep learning - GAN's - autoencoder or feature-based I can send you some papers to wrok on but I prefer u choose the method that gives best results

1. Preprocessing of LiDAR data 2. Implementation of VAE

Preprocessing of LiDAR data Implementation of VAE

I would like to implement a Network Architecture Search (NAS) algorithm to find the best model for an autoencoder. The algorithm needs to follow the Greedy Best First Search steps. more details will be shared over a chat

Improved Generative Adversarial Networks for VHR Remote Sensing Image Classification SAR Image Ship Object Generation and Classification With Improved Residual Conditional Generative Adversarial Network Deep Convolutional Generative Adversarial Network With Autoencoder for Semisupervised SAR Image Classification Enhancing ISAR Resolution by a Generative Adversarial Network

...Technical indidcators are added to the dataset. 5. Fundamental data is added to the dataset: Sentiment analysis with BERT module to calssify the companies headlines. 6. Fourier transforms for the sine wave. 7. ARIMA is used as a technique for predicting time series data. 8. After having added all the features above we now have 112 features (columns): Feature importance test with XGBoost. 9. Autoencoder: more features are generated  not evaluated further 10. The 112 features mentioned for the correlated assets, technical indicators, fundamentl analysis, Fourier, and Arima are evaluated for usefulnes Eigen portfolio with PCA. 84 are left over. 11. GAN and the Reinforcement learning parts are missing here. Are you aware of code using GAN and can it be applied here? 12. LSTM + Le...

an autoencoder needs to be implemented with python using libraries such as pytorch, open3D

We wish to make a sensorimotor autoencoder: That is a neural network that condenses the information found within a sensorimotor feedback loop. We lack the technical expertise to fully evaluate and certainly to execute the idea but can attempt to communicate the idea thusly: Start with a fully connected RNN. Arrange the neurons in layers and remove neurons from inner layers to produce the autoencoder shape. Remove all connections to other cells in the same layer, including the self-connection. This will force connection into the horizontal plane - every node in a layer is connected to all other nodes in all other layers left and right, but are not connected to any notes in it’s own layer - up and down, including itself. Initiate these connections, these weights with a ...

Any variational autoencoder python for any cancer. I need a pyton code that uses some cancer data and applies variational autoencoder.

...understand and apply the sample codes below. 8. Kapitel also tells it in order, I tried to set it up but I got stuck after a while, I tried the sample codes in Python (in Linux-Ubuntu system), but I could not get out of it. 3 steps needs to be taken: 1. enclosed is a new topic: it is about WorldModels, which is a reinforcement learning system that uses a network similar to the U-net (a variational autoencoder) to find a world model. An optimal action is then searched in this model. (If you need the description please inform me to send) The advantage is that the complete system is already implemented. A critical aspect of these systems is that they often only work well under very limited conditions. It would be a matter of finding out whether the system, which copes well with cl...

Please develop an informational article which outlines what an autoencoder is, how it's used within data science, the primary benefits, and how IBM Watson Studio can assist. ~800-1500 words; avoid keyword stuffing to rank on page 1 of SERPs

I need a Graph Autoencoder model built with Pytorch Geometric Temporal. PM for more details.

We need help from a experienced python developer to implement a custom Variational Autoencoder that uses dictionary learning to classify images. we have most of the implementation design figured out, including most of the math. We just need someone who can take care of the quickly. The attached research paper briefly covers the dictionary learning algorithm that we would like to use.

need an expert in python who can train neural network models to recognize basic traffic signs like signals and zebra crossing and stop signs.. after training the model add an autoencoder that is updated by a reward function of the loss which will poison the input data and after receiving the output we need to compare the loss in accuracy. i am attaching the paper being used for my project where instead of mnist dataset we will use a custom dataset which.

I want to do this by $70: (1) edit the error in stacked autoencoder code. This code used as a feature selection algorithm. (2) Add LSTM code as a classification method the first time, then CNN as a classification method another time. Please I want it by proper settings that appropriate for the type of the dataset and the purpose of classification. (3) Count the mean and median for each case in the code for all the 5 folds. (i used Kfold function , 5 folds). Example: the mean of the results of the feature selection with classifier LSTM in (fold1 + fold2 + fold3 +fold4 +fold5). then the mean of the results of the feature selection with classifier CNN in (fold1 + fold2 + fold3 +fold4 +fold5). Notes: The type of dataset contains software metrics. The purpose of this methodology to p...

I'd like to invite some immediate help on my CV project of generating new ...immediate help on my CV project of generating new images by combing two source of other images of animals. I'd like to use autoregressive network with autoencoder and decoder (e.g. VAE), i.e. by linearly interpolating two latent nodes to generate the new images, and explore Image GPT and Deep Dream. Please note I prefer not using GANs in this case. I have had a start but need some expert advice in PyTorch to help me to improve it and walk me through the approach. More details can be provided to right candidates. Please bid ONLY if you can start immediately and deliver by this week, and have solid experience in deep learning, PyTorch and autoencoder etc. as past project examples will be req...

Edit the error in kfold function in my code and do some of the feature selection techniques and apply it on the dataset(chi2, forward feature selection, backward feature selection, Exhaustive feature selection, Random Forest, stacked-autoencoder). work on pycharm with python 3.8.

I need to analyze the importance of the input feature in Autoencoder model.

1: Directed acyclic graph (DAG) structure learning. The source code in python. 2: The aim to modify this algorithm using conditional variational autoencoder method, and compare with two previous methods. The goal to achieve high accuracy.

mathmatics solutions tagging bassed on differences using autoencoder with k-means for text clustering or Hierarchical Clustering need it done as soon as possible

...three parts encoder, an intermediate node, a decoder; an Autoencoder is used at the encoder, an intermediate node, and a decoder. The monkey image is divided into 4 pieces and sent through a network or ISP. Encoding is done at the intermediate node also. MNIST data set (Handwritten digits from 0 to 9) is used. Encoder decoder works jointly. Finally what happens is low SNR regimes also this system results in better SNR when compared to existing techniques. Requirements: Phase I Project (Total budget:20,000 in Indian Rupees & time line:15 days) • An improvisation of NNC paper (NNC – neural network + network coding) should be implemented in real-time on an IoT device. • What novelty & uniqueness can you propose? • Need to optimize autoencoder by u...

I'd like to make an anomaly detection model using CNN-based Autoencoder and LSTM-based Autoencoder. -The equipment subject to fault diagnosis is an air compressor. - You can see the air compressor as follows URL. The attached excel data contains the measurement data set for the normal and abnormal (failure) operating conditions of the air compressor. <normal condition> There are 4 cycles for normal condition data in each excel file. There are 10 Excel files for normal condition. so there are a total of 40 cycles for normal condition data. <abnormal condition> There are 10 types of abnormal (failure) operating conditions of the air compressor. 1. v_belt_eliminated 2. air_big_leakage 3. air_middle_leakage 4. air_small_leakage 5. motor_bearing_damage 6. ...

I'm looking to incorporate dictionary learning into a variational autoencoder for image classification.

...reducing noise then a reinforcement learning algorithm picking the best regression variable weightings. Using augmented random search. (2) World model no augmented random search (3) RNN CNN Auto encoder (4) CNN Auto encoder (5) CNN or RNN with a unsupervised algorithm/ basic reinforcement learning ai (6) CNN or RNN with unsupervised algorithm (7) ANN with and unsupervised algorithm (5) RNN autoencoder Essentially what is created should beat a super learner supervised machine learning Essemble. In all accuracy measures. I have accounted for accuracy R2= 64% explained by predictors, that means if your accuracy is 64% then thats a score of likely 100% atleast from my interpretation. So i wont judge you as long as accuracy

Article : I am currently working on the above article trying to implement the VAE_1. I am working step by step to understand each step. I have implement a standard Variational Autoencoder, but it doesn't seem to work. I would need your help. The person who will be hire on this projet would have to be very good with machine learning in general and with python/pytorch. I would need you to start as soon as possible. Looking forward to working with you!

i have implemented a keras autoencoder in knime and i am feeding it data with no anomalies yet its predicting some as fraud which is not correct i need someone to help me fix that and make sure it gives me 100% accuracy when the autoencoder is only fed data with no anomalies.

My project is about intrusion detection or classification in IoT network traffic. I have proposed something using "Stacked Conditional Variational Autoencoder" for solving data imbalance issue and an DNN for classification which I need to implement in python. And also need to compare results with benchmark algorithms. I have 3 intrusion detection datasets. so I need to build model along with some base models provided in the article I will provide. the base model is like this: they used"Conditional Variational Autoencoder" for generating new data by training the original data. second they merged the newly generated data with original data. 3rd they used DNN algorithm o train that merged data to classify the class/labels.

Hi I have a problem with using autoencoder for recreating fingerprinting images I am using MATLAB and Sparse autoencoder I am using extractHOGFeatures for extracting the features to be trained for autoencoder I got good results on the performance of training and mseError which reaching 0.0039 However when i use autoencoder with testing images i got black color on the whole Reconstructed image I followed this example in MATLAB for autoencoder but I am still facing the problem for Reconstructed images that are with black color for whole images

Write a blog on Variational Autoencoder

Anomaly detection using Autoencoder of streaming data my data having four coloums. 1. Date : 2. Hour 3. Cellname 4. Traffic (Anomaly detection- outcome) The traffic has the repetitive pattern for each day. Each Cellname has different pattern, but it repeats itself every day Traffic is usually high at evening, low at night and medium at day (sinusoidal wave)

hey guys, I have a project regarding one-shot learning for fingerprint. I have already implemented a one-shot learning model based on CNN and the model trained with 600 people's fingers and on test time noisy fingerprints of people were tested. And the model accuracy is satisfactory. I would like to modify my code. I don't wanna take real finge...people were tested. And the model accuracy is satisfactory. I would like to modify my code. I don't wanna take real fingerprint data as input on training or test time. Instead of that, I would like to create a new model that generates auto encoding of the data and this auto encoded data should feed the one-shot learning model, not data itself. So I would like to hire someone who can implement autoencoder for me and connect to...

Time Series Anomoly detection using autoencoder

hey guys, I have a project regarding one-shot learning for fingerprint. I have already implemented a one-shot learning model based on CNN and the model trained with 600 people's fingers and on test time noisy fingerprints of people were tested. And the model accuracy is satisfactory. I would like to modify my code. I don't wanna take real fingerprint data as input on training or tes...of people were tested. And the model accuracy is satisfactory. I would like to modify my code. I don't wanna take real fingerprint data as input on training or test time. Instead of that, I would like to create a new model that generates auto encoding of the data and this auto encoding should feed the one-shot learning model, not data itself. So I would like to hire someone who can implement ...

It has total 3 parts Part 1: denoising: in python implement your denoising method of choice 1) Bilateral filtering 2) Non-local means 3) Denoising autoencoder Part 2: segmentation: in python implement your segmentation method of choice 1) Otsu’s method 2) Watershed transform or region growing 3) Mean-shift clustering 4) Graph-cut 5) Neural network Part 3: practical challenge (vascular segmentation) Note: do not simply call opencv or scipy, or other python library. You must implement the algorithm yourself using primitive array operations in numpy. I will check your code line by line in the .py file you submit, to verify this* Rest I'll send everything in chat. Looking forward for good freelancer here. Thank you

The hypothesis of the project is to do a comparative study of multiple autoencoder models (GAN- Generative Adversarial Network) and to generate your own autoencoder model (GAN- Generative Adversarial Network) for the purpose of image compression. The layers of the CNN needs to be made on our own that would have its own benefits like optimization etc. The main motive of creating your own CNN autoencoder model (GAN- Generative Adversarial Network) for image compression is to justify the advantage of this new model (that would be absolutely novel) over the others in terms of image compression. Language used is Python in Jupyter notebook

I have created a Unet colorization autoencoder. The color space that I work on is Ciel Lab. The network accepts the luminance channel and predicts the a and b channel. In the so far created model, I would like to add some custom loss and metrics. The custom loss is the Perceptual loss and metric is the PSNR to be applied. I am looking for a customization in an existing model.

Create a fully automatic colorization encoder that will turn grayscale into color images, using deep network techniques. More specific I would like to implement a autoencoder, that will have as the encoder part a modified vgg 16 neural network and as a decoder a vgg custom one. The modification that I would like the VGG 16 to have, is to include residual or skip connections with the decoder, such that to pass the extracted features, in a way to enable to have better representation of the extracted feature and achieve better colorization results.

Hello! There is a project regarding the implementation of Variational Autoencoder in python. Do you have experience in it? The code is written, but requires some debuging, and just to finish withing several lines.

...death toll (for the next seven days i.e., if the training dataset has data upto date April 7, 2020; use data till the date of March 31, 2020 as training data and then predict the death toll and number of patients diagnosed as COVID19 patient) using LSTM and Deep-Autoencoder. You should vary the number of inputs/window size (i.e, how many information from the past days should be used as input to predict the next day/seven days) and report all the results in graph. Then conclude which result is the best and why. (for the Deep autoencoder use the stacked version of the Dense layers. You can vary the number of layers from 2 to 10 and report the results of each of the configuration.) I will send you the dataset.(in the attached file) At the end and most important make a repor...

I have a package which can create an embedding from and would like to take this embedding and decode it to create a new dataset. Your deliverable should be an all-encompassing Jupyter notebook which will take a dataset (provided) use the above encoder to create an embedding and then create a new set using the embedding. The pay is strong, so a solid presentable is needed at the end.