While the latent space of a typical GAN consists of input vectors, randomly sampled from the standard Gaussian distribution, the latent space of RPGAN consists of random paths in a generator network. As we show, this design allows to understand factors of variation, captured by different generator layers, providing their natural interpretability. With experiments on standard benchmarks, we demonstrate that RPGAN reveals several interesting insights about the roles that different layers play in the image generation process.
Aside from interpretability, the RPGAN model also provides competitive generation quality and allows efficient incremental learning on new data. Nowadays, deep generative models are an active research direction in the machine learning community.
These methods are not only popular among academicians, but are also a crucial component in a wide range of applications, including image editing [ isolaimagezhuunpaired ]super-resolution [ ledigphoto ]video generation [ wangvideo ] and many others. Along with practical importance, a key benefit of accurate generative models is a more complete understanding of the internal structure of the data.
RPGAN: GANs Interpretability via Random Routing
Insights about the data generation process can result both in the development of new machine learning techniques as well as advances in industrial applications. However, most state-of-the-art generative models employ deep multi-layer architectures, which are difficult to interpret or explain.
While many works investigate interpretability of discriminative models [ zeilervisualizingsimonyandeepmahendranunderstanding ]only a few [ cheninfoganbaugandissect ] address the understanding of generative ones. In traditional GAN generators, the stochastic component that influences individual samples is a noisy input vector, typically sampled from the standard Gaussian distribution. In contrast, RPGAN generators instead use stochastic routing during the forward pass as their source of stochasticity.
For each sample, only one random instance of each layer is activated during generation. In the sections below, we show how RPGAN allows to understand the factors of variation captured by the particular layer and reveals several interesting findings about the image generation process, e. As a practical advantage, RPGANs can be efficiently updated to new data via the simple addition of new instances to the bucket, avoiding re-training the full model from scratch.
Finally, we observe that RPGANs allow the construction of generative models without nonlinearities, which can significantly speed up the generation process for fully-connected layers. With extensive experiments on standard benchmarks we reveal several insights about the image generation process. Many of our insights confirm and extend recent findings from [ baugandissect ]. Note, that our scheme is more general compared to the technique from [ baugandissect ] as RPGAN does not require labeled datasets or pretrained segmentation models.
The rest of this paper is organized as follows. Generative adversarial networks. GANs are currently one of the main paradigms in generative modelling. Since the seminal paper on GANs by [ goodfellowgenerative ]a plethora of alternative loss functions, architectures, normalizations, and regularization techniques were developed [ kurachlarge ].
In essence, GANs consist of two networks — a generator and a discriminator, which are trained jointly in an adversarial manner. In standard GANs, the generation stochasticity is provided by the input noise vector. In RPGANs, we propose an alternative source of stochasticity by using a fixed input but random routes during forward pass in the generator. Specific GAN architectures. Many prior works investigated different design choices for GANs, but to the best of our knowledge, none of them explicitly aimed to propose an interpretable GAN model.
Important differences of RPGAN compared to the works described above is that it uses random routes as its latent space and does not enforce to mimic the latent representations of pretrained classifiers. While interpretability of models based on deep neural networks is an important research direction, most existing work addresses the interpretability of discriminative models.
These works typically aim to understand the internal representations of networks [ zeilervisualizingsimonyandeepmahendranunderstandingdosovitskiygenerating ] or explain decisions produced by the network for particular samples [ sundararajanaxiomaticbachpixelsimonyandeep ].
However, only a few works address interpretability of generative models. A related work by [ baugandissect ] develops a technique that allows to identify which parts of the generator are responsible for the generation of different objects.
In contrast, we propose GANs with alternative source of stochasticity that allows natural interpretation by design. Some of our findings confirm the results from [ baugandissect ]which provides stronger evidence about the responsibilities of different layers in the generation process. Note, that the technique [ baugandissect ] requires a pretrained segmentation network and cannot be directly applied to several benchmarks, e.
In contrast, RPGAN does not require any auxiliary models or supervision and can be applied to any data. For instance, earlier layers aim to detect small texture patterns, while activations in deeper layers typically correspond to semantically meaningful concepts.
Similarly, in our paper we aim to understand the roles that different GAN layers play in image generation.We are happy to open source the code for Real NVPa novel approach to density estimation using deep neural networks that enables tractable density estimation and efficient one-pass inference and sampling.
This model successfully decomposes images into hierarchical features ranging from high-level concepts to low-resolution details. Visualizations are available here. Once you have successfully installed the dependencies, you can start by downloading the repository:. Then do:.
Downloading the small Imagenet dataset is more straightforward and can be done entirely in Shell:. To prepare the LSUN dataset, we will need to use the code associated:. The visualizations and validation set evaluation can be seen through Tensorboard.
Skip to content. Branch: master. Create new file Find file History. Latest commit Fetching latest commit…. Installation python 2.
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Move the research models into a research subfolder Sep 21, Fix Python 3 Syntax Errors en masse. Jan 23, Jan 20, The increasingly photorealistic sample quality of generative image models suggests their feasibility in applications beyond image generation.
We present the Neural Photo Editor, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images.
To tackle the challenge of achieving accurate reconstructions without loss of feature quality, we introduce the Introspective Adversarial Network, a novel hybridization of the VAE and GAN.
Our model efficiently captures long-range dependencies through use of a computational block based on weight-shared dilated convolutions, and improves generalization performance with Orthogonal Regularization, a novel weight regularization method.
Editing photos typically involves some form of manipulating individual pixels, and achieving desirable results often requires significant user expertise. VAEs are probabilistic graphical models that learn to maximize a variational lower bound on the likelihood of the data by projecting into a learned latent space, then reconstructing samples from that space. GANs learn a generative model by training one network, the "discriminator," to distinguish between real and generated data, while simultaneously training a second network, the "generator," to transform a noise vector into samples which the discriminator cannot distinguish from real data.
Both approaches can be used to generate and interpolate between images by operating in a low-dimensional learned latent space, but each comes with its own set of benefits and drawbacks. VAEs have stable training dynamics, but tend to produce images that discard high-frequency details when trained using maximum likelihood. By contrast, GANs have unstable and often oscillatory training dynamics, but produce images with sharp, photorealistic features.
Two key issues arise when attempting to use a latent-variable generative model to manipulate natural images. This simultaneously necessitates an inference mechanism or inference-by-optimization and careful design of the model architecture, as there is a tradeoff between reconstruction accuracy and learned feature quality that varies with the size of the information bottleneck.
In the fully unsupervised setting, however, such semantically meaningful output features are generally controlled by an entangled set of latents which cannot be directly manipulated. In this paper, we present the Neural Photo Editor, an interface that handles both of these issues, enabling a user to make large, coherent changes to the output of unsupervised generative models by indirectly manipulating the latent vector with a "contextual paintbrush.
Complementary to the Neural Photo Editor, we develop techniques to improve on common design tradeoffs in generative models. Instead of changing individual pixels, the interface backpropagates the difference between the local image patch and the requested color, and takes a gradient descent step in the latent space to minimize that difference.
This step results in globally coherent changes that are semantically meaningful in the context of the requested color change. This technique enables exploration of samples generated by the network, but fails when applied directly to existing photos, as it relies on the manipulated image being completely controlled by the latent variables, and reconstructions are usually imperfect. We circumvent this issue by introducing a simple masking technique that transfers edits from a reconstruction back to the original image.
We take the output image to be a sum of the reconstruction, and a masked combination of the requested pixel-wise changes and the reconstruction error:. The mask is designed to allow changes to the reconstruction to show through based on their magnitude. This relaxes the accuracy constraints by requiring that the reconstruction be feature-aligned rather than pixel-perfect, as only modifications to the reconstruction are applied to the original image.
As long as the reconstruction is close enough and interpolations are smooth and plausible, the system will successfully transfer edits. This method adds minimal computational cost to the underlying latent space exploration and produces convincing changes of features including hair color and style, skin tone, and facial expression. A video of the interface in action is available online. Complementary to the Neural Photo Editor, we introduce the Introspective Adversarial Network IANa novel hybridization of the VAE and GAN motivated by the need for an image model with photorealistic outputs that achieves high-quality reconstructions without loss of representational power.
There is typically a design tradeoff between these two goals related to the size of the latent space: a higher-dimensional latent space i. We thus seek techniques to improve the capacity of the latent space without increasing its dimensionality. Central to the IAN is the idea that features learned by a discriminatively trained network tend to be more expressive those learned by an encoder network trained via maximum likelihood i.
As the Neural Photo Editor relies on high-quality reconstructions, the inference capacity of the underlying model is critical. Accordingly, we use the discriminator of the GAN, Das a feature extractor for an inference subnetwork, Ewhich is implemented as a fully-connected layer on top of the final convolutional layer of the discriminator.
L i m gthe L 1 pixel-wise reconstruction loss, which we prefer to the L 2 reconstruction loss for its higher average gradient. L f e a t u r ethe feature-wise reconstruction loss, evaluated as the L 2 difference between the original and reconstruction in the space of the hidden layers of the discriminator. L a d vthe ternary adversarial loss, a modification of the adversarial loss that forces the discriminator to label a sample as real, generated, or reconstructed as opposed to a binary real vs.
The discriminator is updated solely using the ternary adversarial loss. During each training step, the generator produces reconstructions G E X using the standard VAE reparameterization trick from data X and random samples G Zwhile the discriminator observes X as well as the reconstructions and random samples, and both networks are simultaneously updated.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Note: I don't have access to the original dataset now, so I'm not entirely sure about the detailed filenames and paths. If your data is in a directory other than. Note: N,M is the number of micro patches in a macro patch, S is the macro patch size.
Note: This experiment is a little unstable, I usually run 3 to 5 epochs and pick the best model by personal preference.
Here's an exmple config:. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. Python Shell. Python Branch: master.Generations from BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling
Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit. Latest commit a Mar 27, You may need to write your own workaround if you face errors with other TF versions. Install cube2sphere library with pip install cube2sphere. I believe there was a script somewhere though Convert TF-Records To speed up training, we process the images into tfrecords: Run the one s you need python.
Here's an exmple config: python main. Note: These models are trained with Tensorflow 1. The structure of the checkpoint directory is:. Though our implementation theretically supports a patch size H! Please let me know or create a pull request with a fix if you are willing if you face any problem while using this feature! In general, with smaller micro patches, as the computation graph becomes too complex for Tensorflow, it will take lots of time an GPU memory to build the graph.
The performance may be improved with Pyotrch and using torch. This implementation is different to our private codebase, please kindly let me know if you found anything bizzard. The coordinate generation part looks complicated since I made it generic to different coordinate designs. Special Thanks The following open-source repositories largely facilitate our research!
How to Train StyleGAN to Generate Realistic Faces
You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Sep 23, Energy-based models EBMs are powerful probabilistic models, but suffer from intractable sampling and density evaluation due to the partition function.
As a result, inference in EBMs relies on approximate sampling algorithms, leading to a mismatch between the model and inference. Motivated by this, we consider the sampler-induced distribution as the model of interest and maximize the likelihood of this model. This yields a class of energy-inspired models EIMs that incorporate learned energy functions while still providing exact samples and tractable log-likelihood lower bounds.
We describe and evaluate three instantiations of such models based on truncated rejection sampling, self-normalized importance sampling, and Hamiltonian importance sampling. Moreover, EIMs allow us to generalize a recent connection between multi-sample variational lower bounds and auxiliary variable variational inference.
We show how recent variational bounds can be unified with EIMs as the variational family. Dieterich Lawson. George Tucker. Bo Dai. Rajesh Ranganath. Variational Inference is a powerful tool in the Bayesian modeling toolki Recent work used importance sampling ideas for better variational bounds Despite the advances in the representational capacity of approximate dis Computing the partition function Z of a discrete graphical model is a fu We introduce a framework for representing a variety of interesting probl Free energy perturbation FEP was proposed by Zwanzig more than six dec Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.
Because of the intractability of general EBMs, practical implementations rely on approximate sampling procedures e. This creates a mismatch between the model and the approximate inference procedure, and can lead to suboptimal performance and unstable training when approximate samples are used in the training procedure. Currently, most attempts to fix the mismatch lie in designing better sampling algorithms e. Instead, we bridge the gap between the model and inference by directly treating the sampling procedure as the model of interest and optimizing the log-likelihood of the the sampling procedure.
This shift in perspective aligns the training and sampling procedure, leading to principled and consistent training and inference. To accomplish this, we cast the sampling procedure as a latent variable model. To illustrate this, we develop and evaluate energy-inspired models based on truncated rejection sampling Algorithm 1self-normalized importance sampling Algorithm 2and Hamiltonian importance sampling Algorithm 3. Our second contribution is to show that EIMs provide a unifying conceptual framework to explain many advances in constructing tighter variational lower bounds for latent variable models e.
Previously, each bound required a separate derivation and evaluation, and their relationship was unclear.
Based on general results for auxiliary latent variables, this immediately gives rise to a variational lower bound with a characterization of the tightness of the bound.
Furthermore, this unified view highlights the implicit potentially suboptimal choices made and exposes the reusable components that can be combined to form novel variational lower bounds.
Concurrently, Domke and Sheldon note a similar connection, however, their focus is on the use of the variational distribution for posterior inference.
To illustrate this, we build models with truncated rejection sampling, self-normalized importance sampling, and Hamiltonian importance sampling and evaluate them on synthetic and real-world tasks.
These models can be fit by maximizing a tractable lower bound on their log-likelihood. We show that EIMs with auxiliary variable variational inference provide a unifying framework for understanding recent tighter variational lower bounds, simplifying their analysis and exposing potentially sub-optimal design choices.Generative Adversarial Networks GAN is an architecture introduced by Ian Goodfellow and his colleagues in for generative modeling, which is using a model to generate new samples that imitate an existing dataset.
It is composed of two networks: the generator that generates new samples, and the discriminator that detects fake samples. The generator tries to fool the discriminator while the discriminator tries to detect samples synthesized by the generator. Once trained, the generator can be used to create new samples on demand.
GANs have quickly become popular due to their various interesting applications such as style transfer, image-to-image translation or video generation. This architecture is particularly well-suited to generating faces, for example. In this report, I will explain what makes StyleGAN architecture a good choice, how to train the model, and some results from training.
If you are interested in a more complete explanation of StyleGAN, you may check out this great article and skip to the next section. This section will explain what are the features in the StyleGAN architecture that makes it so effective for face generation. Previous GAN models have already shown to be able to generate human faces, but one challenge is being able to control some features of the generated images, such as hair color or pose.
StyleGAN attempts to tackle this challenge by incorporating and building on progressive training to modify each detail level separately. In doing so, it can control visual features expressed in each detail level, from coarse features such as pose and face shape, to finer details such as eye color and nose shape, without affecting other levels.
Progressive training was first introduced in the ProGAN architecture with the objective to generate high-definition images. In progressive training, the model is first trained on low-resolution images, such as 8x8, then the input image resolution is progressively doubled by adding new higher-resolution layers to the model during training. In doing so, the models can rapidly learn coarse details in the early stages of training, and finer details later on, instead of having to learn all scales of detail simultaneously.
Because the features were not trained separately, it is difficult to try to tweak one specific feature without affecting several others. StyleGAN extends upon progressive training with the addition of a mapping network that encodes the input into a feature vector whose elements control different visual features, and style modules that translate the previous vector into its visual representation.
By using separate feature vectors for each level, the model is able to combine multiple features: for example, from two generated images, the model can use coarse level features from the first, fine detail features from the second, to generate a third that combines the two.
With progressive training and separate feature mappings, StyleGAN presents a huge advantage for this task. The model requires less training time than other powerful GAN networks to produce high quality realistic-looking images.
Moreover, in face generation where there are many different features that each have several instances, this architecture is particularly suited because the model is able to learn facial features separately, without being influenced by correlation between feature levels, to generate images with good variety. Obtaining realistic and varied images are the two main objectives of this challenge, where I have limited resources to train my model, which is why StyleGAN became my architecture of choice.
I used the CelebA dataset to train my model. CelebA containsface images of 10, different celebrities. The original dataset is annotated with binary features such as eyeglasses or big nosebut we will only use the images themselves for face generation.
The images in the dataset are of dimension x Since we want to generate square images, we crop the images. To do so, we assume the face lies near the center of the image and take the center crop.
We resize the images to accommodate progressive training, as described previously, from 8x8 until x, the chosen final output size. Note that this technique is capable of training models with x images, but this would require over a month of GPU training, and a resolution of at least 64x64 already gives good visual results. Each image is resized to have a copy in dimensions 8x8, 16x16, 32x32, 64x64 and x, so that the trained generator will generate images in dimension x Other possible data processing methods, which I have not used, are detecting and cropping the images to the faces more closely, and to remove examples where the face is not facing front.
To train my own model, I found a great implementation of StyleGAN on Github in my favorite machine learning framework, with understandable code. Training is done in the same fashion as traditional GAN networks, with the added task of progressive training.
I used an Adam optimizer with learning rate 0. I use a batch size of 16, because of memory constraints, and a code size ofthat is the random noise vector inputted in the generator is of size 1x For the loss function, I use Wasserstein loss.
For progressive training, each dimension size trains on image instances before increasing the size, until reaching dimension size x where I keep training the model until convergence.
We can use three techniques for regularization.Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. I've seen this in a bunch of places. What's the point to flush right before closedoesn't the latter imply the former? To be honest, I'm not sure.
The online documentation isn't very informative, and it does not explicitly mention that a close call will flush to disk:. I guess it's a superstitious pattern I've seen repeated enough time that I've assumed it's necessary.
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The online documentation isn't very informative, and it does not explicitly mention that a close call will flush to disk: close Close this file. All open objects will become invalid. Defaults to 0.