I would like to perform the task described in A Neural Algorithm of Artistic Style which applies the style of an image to a different image. This paper dates back to 2015 and there has been a lot of progress in the field of computer vision since then. What is the most up-to-date paper/github repository for this task? I have searched on paperswithcode, but there are no benchmarks or ranking of papers related to the task of style transfer.

I'm reading Style GAN paper and in style gan, we are passing the vector "W" through "A". I didn't understand what exactly the "A" is doing. In paper, it's mentioned Affine transformation. But I didn't understand how is it producing scaled and bias vectors.

I'm trying to choose what neural style transfer architecture to use but I can't find a centralized list of all possible architectures I could choose from. Is there a place I could find this or could someone give me a summary of popular architectures that I can look in to?

Normally, a neural style operation works by taking a content image and a style image. A third image is optimized to have the same content as the content image and the same style as the style image by optimizing the content of the content image and the style of the style image. However, for one particular application, I want to do "content transfer". Content transfer means that in particular, out of the two images designated content image and style image …

I am writing an implementation of style transfer by loading a vgg model from keras and supplying it to a tensorflow model. I am using an adam optimizer. The loss function is reducing but it is very slow and plateaus off at about 108. Additionally the generated image color seems to be changing correctly but it is still clearly noise. Also, the style loss is huge (order of 108) whereas content loss is much smaller(order of 105). This is weird …

I am using this pytorch script to learn and understand neural style transfer. I understood most part of the code but having some hard time understanding some parts of the code. In line 15 Its not clear to me how model_activations work. I made a sample style tensor of the shape style.shape -> torch.Size([3, 300, 374]) and tried this sample code first without layers dict. x = style x = x.unsqueeze(0) for name,layer in model._modules.items(): x = layer(x) print(x.shape) Output: …

Currently implementing the style transfer model proposed in the article Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization. The model takes two RGB images as input: one content image and one style image and then generates a new image depicting the content image in the style of the given style image. The model has an autoencoder-like structure: a pretrained VGG19 model is used as encoder. In the bottleneck an AdaIn layer is used that takes as input the encoded …

I am currently working on the automation of recurring reports (weekly 30-50 pages reports for around 100 districts). Those reports have a mostly fixed form : maps, graphs, data tables and small zone of text. Apart for some discussion around colors and legends, it isn't difficult to automate the production of maps / graphs / tables. (I work with Rmarkdown if you want to know) However, for the text, a simple approach like writing 'r value' in markdown to produce …

In the loss function between generated image and content image, we calculate the error taking the only activation of the corresponding channel but for calculation of loss function between style and generated image we calculate the gram matrix. But we should do the same for the loss function between content and generated image to find the correlation between different channels. So why we do not use gram matrix in loss function calculation between content and generated? Computational efficiency is not …

From what I understand, BERT provides contextualized embeddings that are not deterministic the way Word2Vec embeddings (i.e. the word "Queen" doesn't always produce the same vector, it'll be different depending on the context) Is there a way to "reverse" these contextualized embeddings to produce an output related to the original content of the text? For instance, how would I do machine translation, or style transfer?

I am training a deep-learning style transfer model with the pretrained-VGG19 CNN. My aim is to use it in my Android app for personal purposes with Google Firebase Machine Learning Kit (which would host my .H5 model to make it usable by my Android app). The maximum .H5 model file's size allowed by Machine Learning Kit is: 8MB. However when I save the whole VGG19 model, I end with 80MB... So I can't use it. Since only some layers of …

Say, for instance, if I had image data from one high resolution digital camera and wanted to make it look like it was taken from another, lower resolution, digital camera? Would training input/output pairs of overlapping images be a good way to do this? What is this technique called? For example, say I wanted to be able to count benches in parks in LOW resolution imagery. Could I go through these sample images and create an appropriate dataset of high …

I've been trying to implement neural style transfer as described in this paper here According to the paper, we can visualise the information at different processing stages in the CNN by reconstructing the input image from only knowing the network’s responses in a particular layer. My question is, how exactly does one go about reconstructing image from a single layer? I'm implementing this in pytorch. I've the output from layer conv4_2 stored in a tensor of shape [1,512,50,50] but how …

I'm trying to utilize AWS EC2 p2.xlarge instance to convert images using style-transfer code given in this git repo:https://github.com/lengstrom/fast-style-transfer.git, yet when the input file becomes large, I keep running into this error: 2018-09-12 02:55:25.797741: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA 2018-09-12 02:55:25.797880: I tensorflow/core/common_runtime/process_util.cc:69] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance. Segmentation fault (core dumped) This happens …

As far as I understand it, the Neural Style Transfer uses a content image and a style image, and generate a new image based on the two images. It tries to find a set of pixel values such that the cost function J(C, S) is minimized. It does not have any labels associated in advance, but it has an output (generated image) that should be the target of the learning. However, I'm not sure if this is considered supervised or …

I have set of night images which I will be using for self driving. But I want to convert those images into day images. I have developed algorithm based on day image but it is not good for night images , so I want to convert night images to day images then feed into the network. As far as I have explored image colourization techniques of grey scale image ( converting night image to black and white and then coloring …