More often than not, these systems build upon generative adversarial networks GANswhich are two-part AI models consisting of a generator that creates samples and a discriminator that attempts to differentiate between the generated samples and real-world samples. This unique arrangement enables GANs to achieve impressive feats of media synthesis, from composing melodies and swapping sheep for giraffes to hallucinating footage of ice skaters and soccer players.
The evolution of GANs — which Facebook AI research director Yann LeCun has called the most interesting idea of the decade — is somewhat long and winding, and very much continues to this day. They have their deficiencies, but GANs remain one of the most versatile neural network architectures in use today. Goodfellow has often stated that he was inspired by noise-contrastive estimation, a way of learning a data distribution by comparing it against a defined noise distribution i. Dalle Molle Institute for Artificial Intelligence Research co-director Juergen Schmidhuber advocated predictability minimization, a technique that models distributions through an encoder that maximizes the objective function the function that specifies the problem to be solved by the system minimized by a predictor.
Again, GANs consist of two parts: generators and discriminators. The generator model produces synthetic examples e. GANs train in an unsupervised fashion, meaning that they infer the patterns within data sets without reference to known, labeled, or annotated outcomes.
In practice, GANs suffer from a number of shortcomings owing to their architecture. The simultaneous training of generator and discriminator models is inherently unstable. Alternatively, the generator collapses, and it begins to produce data samples that are largely homogeneous in appearance. The generator and discriminator also run the risk of overpowering each other.
One entails building multiple discriminator into a model and fine-tuning them on specific data. Another involves feeding discriminator dense embedding representations, or numerical representations of data, so that they have more information from which to draw. StyleGAN, a model Nvidia developed, has generated high-resolution head shots of fictional people by learning attributes like facial pose, freckles, and hair.
A newly released version — StyleGAN 2 — makes improvements with respect to both architecture and training methods, redefining the state of the art in terms of perceived quality. The coauthors of a related study proposed a system — StoryGAN — that synthesizes storyboards from paragraphs.
Such models have made their way into production. Startup Vue.
DeepMind Uses GANs to Convert Text to Speech
From snapshots of apparel, it can generate model images in every size up to five times faster than a traditional photo shoot. Elsewhere, GANs have been applied to the problems of super-resolution image upsampling and pose estimation object transformation. Tang says one of his teams used GANs to train a model to upscale bypixel satellite imagery to 1, by 1, pixels, and to produce images that appear as though they were captured from alternate angles.
Scientists at Carnegie Mellon last year demoed Recycle-GANa data-driven approach for transferring the content of one video or photo to another. When trained on footage of human subjects, the GAN generated clips that captured subtle expressions like dimples and lines that formed when subjects smiled and moved their mouths. Predicting future events from only a few video frames — a task once considered impossible — is nearly within grasp thanks to state-of-the-art approaches involving GANs and novel data sets.
One of the newest papers on the subject from DeepMind details recent advances in the budding field of AI clip generation. In a twist on the video synthesis formula, Cambridge Consultants last year demoed a model called DeepRay that invents video frames to mitigate distortion caused by rain, dirt, smoke, and other debris.
GANs are capable of more than generating images and video footage. Scientists at the Maastricht University in the Netherlands created a GAN that produces logos from one of 12 different colors. The machine learning model underpinning GauGAN was trained on more than one million images from Flickr, imbuing it with an understanding of the relationships among over objects including snow, trees, water, flowers, bushes, hills, and mountains.
In practice, trees next to water have reflections, for instance, and the type of precipitation changes depending on the season depicted. Google and Imperial College London researchers recently set out to create a GAN-based text-to-speech system capable of matching or besting state-of-the-art methods.
Their proposed system — GAN-TTS — consists of a neural network that learned to produce raw audio by training on a corpus of speech with pieces of encoded phonetic, duration, and pitch data.Generative Adversarial Networks GANs have revolutionized high-fidelity image generation, making global headlines with their hyperrealistic portraits and content-swapping, while also raising concerns with convincing deepfake videos.
Now, DeepMind researchers are expanding GANs to audio, with a new adversarial network approach for high fidelity speech synthesis. Text-to-Speech TTS is a process for converting text into a humanlike voice output. One of the most commonly used TTS network architectures is WaveNet, a neural autoregressive model for generating raw audio waveforms.
DeepMind explored raw waveform generation using GANs composed of a conditional generator for producing raw speech audio and an ensemble of discriminators for analyzing the audio.
In the GAN-TTS process the input G is a sequence of human speech with linguistic features encoded phonetic and duration information and pitch information logarithmic fundamental frequency at Hz. The generator learns how to convert the linguistic features and pitch information to raw audio. The output is a raw waveform at 24kHz.
In addition to their data augmentation effect, RWDs are more suitable for analyzing audio realism and how well it corresponds to the target utterance. The discriminator is composed of DBlocks, and the entire structure is shown below:.
DeepMind Extends Abilities Of Machines To Generate High Fidelity Speech With GAN-TTS
DeepMind compared their model with previous research using mean opinion scores MOS to evaluate performance. Notify me of follow-up comments by email. Notify me of new posts by email. Residual blocks — GBlock used in the model. Residual blocks — Conditional left and unconditional right DBlocks used in model.
Generative adversarial networks: What GANs are and how they’ve evolved
Multiple Random Window Discriminator architecture. Share this: Twitter Facebook. Like this: Like Loading Leave a Reply Cancel reply Your email address will not be published. Comment Name Email Website Notify me of follow-up comments by email.
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Though I haven't got improvements over Saito's approach  yet, but the GAN-based models described in  should be achieved by the following configurations:.
Model checkpoints will be saved at. The repository doesn't try to reproduce same results reported in their papers because 1 data is not publically available and 2.
Instead, I tried same ideas on different data with different hyper parameters. 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. No description, website, or topics provided. Jupyter Notebook Other. Jupyter Notebook Branch: master.
Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again. Latest commit Fetching latest commit…. Computing adversarial loss on mgc features except for first few dimensions seems to be working good. As described in saitoasjaI confirmed that using 0-th and 1-th mgc for computing adversarial loss affects speech quality.
If you see cuda runtime error 2 : out of memorytry smaller batch size. This will enable generator to use Gaussian noise as input. Linguistic features are concatenated with the noise vector.
The discriminator uses linguistic features as condition.Generative adversarial network GAN is a new idea for training models, in which a generator and a discriminator compete against each other to improve the generation quality.
Recently, GAN has shown amazing results in image generation, and a large amount and a wide variety of new ideas, techniques, and applications have been developed based on it. Although there are only few successful cases, GAN has great potential to be applied to text and speech generations to overcome limitations in the conventional methods.
The tutorial includes two parts. The first part provides a thorough review of GAN. We will first introduce GAN to newcomers and describe why it is powerful in generating objects with sophisticated structures, for example, images, sentences, and speech. Then, we will introduce the approaches that aim to improve the training procedure and the variants of GAN beyond simply generating random objects. The second part of this tutorial will focus on the applications of GAN on speech and natural language.
However, speech signals are temporal sequences which have very different nature from images. We will describe how to apply GAN on speech signal processing, including text-to-speech synthesis, voice conversion, speech enhancement, and domain adversarial training on speech-related tasks. The major challenge for applying GAN on natural language is its discrete nature words are usually represented by one-hot encodingswhich makes the original GAN fails. We will review a series of approaches dealing with this problem, and finally demonstrate the applications of GAN on chat-bot, abstractive summarization, and text style transformation.
Skip to main content. Thumbs Up up 1 user has voted: Peng Zhang. Hung-yi Lee, Yu Tsao. Send Author a Private Message. Accessed: Apr.
Hung-yi Lee, Yu Tsao, GANs have achieved state-of-the-art results in image and video generation, and have been successfully applied for unsupervised feature learning among many other applications. Generative adversarial networks have seen rapid development in recent years, however, their audio generation prowess has largely gone unnoticed. Text-to-Speech TTS is a process for converting text into a humanlike voice output. Many audio generation models operate in the waveform domain.
They directly model the amplitude of the waveform as it evolves over time. Autoregressive models achieve this by factorising the joint distribution into a product of conditional distributions. Whereas, the invertible feed-forward model can be trained by distilling an autoregressive model using probability density distillation. Models like Deep Voice 2 and 3 and Tacotron 2, in the past, have achieved some accuracy by first generating a representation of the desired output, and then using a separate autoregressive model to turn it into a waveform and fill in any missing information.
However, since the outputs are imperfect, the waveform model has the additional task of correcting any mistakes. GANs too have been explored. The authors believe that GANs have not yet been applied for large scale audio generation operations. Its feed-forward generator is a convolutional neural network, as shown in the figure above, is coupled with an ensemble of multiple discriminators which evaluate the generated and real audio based on multi-frequency random windows.
The inner workings of the architecture in both generator and discriminator can be summarised as follows:.
They posit that RWDs work much better than the full discriminator because of the relative simplicity of the distributions that the former must discriminate between, and the number of different samples one can draw from these distributions.
GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to state-of-the-art models, and unlike autoregressive models, it is highly parallelizable thanks to an efficient feed-forward generator.
Though the widely popular WaveNet has been around for a while, it largely depends on the sequential generation of one audio sample at a time, which is undesirable for present-day applications. GAN s, however, with their parallelizable traits, make for a much better option for generating audio from text. I have a master's degree in Robotics and I write about machine learning advancements.
Ram Sagar I have a master's degree in Robotics and I write about machine learning advancements. Share This. Our Upcoming Events.Here's a sampling of GAN variations to give you a sense of the possibilities. In a progressive GAN, the generator's first layers produce very low resolution images, and subsequent layers add details. This technique allows the GAN to train more quickly than comparable non-progressive GANs, and produces higher resolution images.
Conditional GANs train on a labeled data set and let you specify the label for each generated instance. Image-to-Image translation GANs take an image as input and map it to a generated output image with different properties.
For example, we can take a mask image with blob of color in the shape of a car, and the GAN can fill in the shape with photorealistic car details. Similarly, you can train an image-to-image GAN to take sketches of handbags and turn them into photorealistic images of handbags.
In these cases, the loss is a weighted combination of the usual discriminator-based loss and a pixel-wise loss that penalizes the generator for departing from the source image. CycleGANs learn to transform images from one set into images that could plausibly belong to another set. For example, a CycleGAN produced the righthand image below when given the lefthand image as input. It took an image of a horse and turned it into an image of a zebra. The training data for the CycleGAN is simply two sets of images in this case, a set of horse images and a set of zebra images.
The system requires no labels or pairwise correspondences between images. For more information see Zhu et al,which illustrates the use of CycleGAN to perform image-to-image translation without paired data.
Text-to-image GANs take text as input and produce images that are plausible and described by the text.
For example, the flower image below was produced by feeding a text description to a GAN. Super-resolution GANs increase the resolution of images, adding detail where necessary to fill in blurry areas. For example, the blurry middle image below is a downsampled version of the original image on the left.
Given the blurry image, a GAN produced the sharper image on the right:. The GAN-generated image looks very similar to the original image, but if you look closely at the headband you'll see that the GAN didn't reproduce the starburst pattern from the original.
Instead, it made up its own plausible pattern to replace the pattern erased by the down-sampling. GANs have been used for the semantic image inpainting task. In the inpainting task, chunks of an image are blacked out, and the system tries to fill in the missing chunks. Yeh et al, used a GAN to outperform other techniques for inpainting images of faces:. Not all GANs produce images. For example, researchers have also used GANs to produce synthesized speech from text input.
For more information see Yang et al, Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4. For details, see the Google Developers Site Policies. Generative Adversarial Networks.Converting natural language text descriptions into images is an amazing demonstration of Deep Learning. Text classification tasks such as sentiment analysis have been successful with Deep Recurrent Neural Networks that are able to learn discriminative vector representations from text.
In another domain, Deep Convolutional GANs are able to synthesize images such as interiors of bedrooms from a random noise vector sampled from a normal distribution. The focus of Reed et al. Conditional-GANs work by inputting a one-hot class label vector as input to the generator and discriminator in addition to the randomly sampled noise vector. This results in higher training stability, more visually appealing results, as well as controllable generator outputs.
The difference between traditional Conditional-GANs and the Text-to-Image model presented is in the conditioning input. Word embeddings have been the hero of natural language processing through the use of concepts such as Word2Vec.
Word2Vec forms embeddings by learning to predict the context of a given word. Reed et al. In addition to constructing good text embeddings, translating from text to images is highly multi-modal. Another example in speech is that there are many different accents, etc. Multi-modal learning is also present in image captioning, image-to-text.
However, this is greatly facilitated due to the sequential structure of text such that the model can predict the next word conditioned on the image as well as the previously predicted words.
Multi-modal learning is traditionally very difficult, but is made much easier with the advancement of GANs Generative Adversarial Networksthis framework creates an adaptive loss function which is well-suited for multi-modal tasks such as text-to-image. The picture above shows the architecture Reed et al. The most noteworthy takeaway from this diagram is the visualization of how the text embedding fits into the sequential processing of the model.
In the Generator network, the text embedding is filtered trough a fully connected layer and concatenated with the random noise vector z. In this case, the text embedding is converted from a x1 vector to x1 and concatenated with the x1 random noise vector z. On the side of the discriminator network, the text-embedding is also compressed through a fully connected layer into a x1 vector and then reshaped into a 4x4 matrix and depth-wise concatenated with the image representation.
This image representation is derived after the input image has been convolved over multiple times, reduce the spatial resolution and extracting information. This embedding strategy for the discriminator is different from the conditional-GAN model in which the embedding is concatenated into the original image matrix and then convolved over.
One general thing to note about the architecture diagram is to visualize how the DCGAN upsamples vectors or low-resolution images to produce high-resolution images. You can see each de-convolutional layer increases the spatial resolution of the image. Additionally, the depth of the feature maps decreases per layer. Lastly, you can see how the convolutional layers in the discriminator network decreases the spatial resolution and increase the depth of the feature maps as it processes the image.
An interesting thing about this training process is that it is difficult to separate loss based on the generated image not looking realistic or loss based on the generated image not matching the text description. The authors of the paper describe the training dynamics being that initially the discriminator does not pay any attention to the text embedding, since the images created by the generator do not look real at all.
Once G can generate images that at least pass the real vs. The discriminator is solely focused on the binary task of real versus fake and is not separately considering the image apart from the text. The most interesting component of this paper is how they construct a unique text embedding that contains visual attributes of the image to be represented.
This vector is constructed through the following process:. The loss function noted as equation 2 represents the overall objective of a text classifier that is optimizing the gated loss between two loss functions. These loss functions are shown in equations 3 and 4. The two terms each represent an image encoder and a text encoder. The image encoder is taken from the GoogLeNet image classification model. This classifier reduces the dimensionality of images until it is compressed to a x1 vector.
Essentially, the vector encoding for the image classification is used to guide the text encodings based on similarity to similar images.