Text to Image Synthesis using Generative Adversarial Networks This is the official code for Text to Image Synthesis using Generative Adversarial Networks . capability of our model to generate plausible images of birds and flowers from Xinchen Yan ∙ 05/17/2016 ∙ by Scott Reed, et al. We used the same base learning rate of 0.0002, and used the ADAM solver (Ba & Kingma, 2015) with momentum 0.5. However, training the GAN models requires a large amount of pairwise image-text data, which is extremely labor-intensive to collect. Exploring models and data for image question answering. fetch relevant images given a text query or vice versa. A common property of all the results is the sharpness of the samples, similar to other GAN-based image synthesis models. Impressively, the model can perform reasonable synthesis of completely novel (unlikely for a human to write) text such as “a stop sign is flying in blue skies”, suggesting that it does not simply memorize. For text features, we first pre-train a deep convolutional-recurrent text encoder on structured joint embedding of text captions with 1,024-dimensional GoogLeNet image embedings (Szegedy et al., 2015) as described in subsection 3.2. The text classifier induced by the learned correspondence function. convolutional generative adversarial networks (GANs) have begun to generate developed a deep Boltzmann machine and jointly modeled images and text tags. share. 0 Among the many applications of GAN, image synthesis is the most well-studied one, and research in this area has already … In the generator G, first we sample from the noise prior z∈RZ∼N(0,1) and we encode the text query t using text encoder φ. (2016). 論文紹介 S. Reed et al. Genera-ve Adversarial Text-to-Image Synthesis (ICML’16) Text to Image Synthesis Using Generative Adversarial Networks. a.k.a StackGAN (Generative Adversarial Text-to-Image Synthesis paper) to emulate it with pytorch (convert python3.x) 0 Report inappropriate Github: myh1000/dcgan.label-to-image Deep captioning with multimodal recurrent neural networks (m-rnn). Fortunately, deep learning has enabled enormous progress in both subproblems - natural language representation and image synthesis - in the previous several years, and we build on this for our current task. different pose). 1.1 Text to Image Synthesis One of the most common and challenging problems in Natural Language Processing and Computer Vision is that of image captioning: given an image, a text description of the image must be produced. Motivated by these works, we aim to learn a mapping directly from words and characters to image pixels. 10/08/2016 ∙ by Scott Reed, et al. ∙ Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., The generator network is denoted G:RZ×RT→RD, the discriminator as D:RD×RT→{0,1}, where T is the dimension of the text description embedding, D is the dimension of the image, and Z is the dimension of the noise input to G. Farhadi, A., Endres, I., Hoiem, D., and Forsyth, D. Fu, Y., Hospedales, T. M., Xiang, T., Fu, Z., and Gong, S. Transductive multi-view embedding for zero-shot recognition and It has been found to work better in practice for the generator to maximize log(D(G(z))) instead of minimizing log(1−D(G(z))). capability of our model to generate plausible images of birds and flowers from Dosovitskiy et al. By content, we mean the visual attributes of the bird itself, such as shape, size and color of each body part. We demonstrate the ∙ where {(vn,tn,yn):n=1,...,N} is the training data set, Δ is the 0-1 loss, vn are the images, tn are the corresponding text descriptions, and yn are the class labels. Denton et al. Disentangling the style by GAN-INT-CLS is interesting because it suggests a simple way of generalization. There has been a drastic growth of research in Generative Adversarial Nets (GANs) in the past few years. Browse our catalogue of tasks and access state-of-the-art solutions. Lajanugen Logeswaran ∙ Low-resolution images are first generated by our Stage-I GAN (see Figure 1(a)). With a trained generator and style encoder, style transfer from a query image x onto text t proceeds as follows: where ^x is the result image and s is the predicted style. The description embedding φ(t), is first compressed using a fully-connected layer to a small dimension (in practice we used 128) followed by leaky-ReLU and then concatenated to the noise vector, , we perform several layers of stride-2 convolution with spatial batch normalization. Multimodal learning with deep boltzmann machines. ∙ This is a pytorch implementation of Generative Adversarial Text-to-Image Synthesis paper, we train a conditional generative adversarial network, conditioned on text descriptions, to generate images that correspond to the description.The network architecture is shown below (Image from [1]). Reed et al. Realistic Bubbly Flow Images. Zhu et al. Technical report, 2016c. By learning to optimize image / text matching in addition to the image realism, the discriminator can provide an additional signal to the generator. Fortunately, deep learning has enabled enormous progress in both subproblems - natural language representation and image synthesis - in the previous several years, and we build on this for our current task. internal covariate shift. ), we can naturally model this phenomenon since the discriminator network acts as a “smart” adaptive loss function. The reverse direction (image to text) also suffers from this problem but learning is made practical by the fact that the word or character sequence can be decomposed sequentially according to the chain rule; i.e. Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one. By conditioning both generator and discriminator on side information (also studied by Mirza & Osindero (2014) and Denton et al. To achieve this, one can train a convolutional network to invert G to regress from samples ^x←G(z,φ(t)) back onto z. CUB has 150 train+val classes and 50 test classes, while Oxford-102 has 82 train+val and 20 test classes. and Fidler, S. Aligning books and movies: Towards story-like visual explanations by Generating photo-realistic images from text is an important problem and has tremendous applications, including photo-editing, computer-aided design, \etc.Recently, Generative Adversarial Networks (GAN) [8, 5, 23] have shown promising results in synthesizing real-world images. convolutional generative adversarial networks (GANs) have begun to generate Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. The only difference in training the text encoder is that COCO does not have a single object category per class. The training image size was set to 64×64×3. Generative Adversarial Networks (GANs) can be applied to image generation, image-to-image translation and text-to-image synthesis tasks all of which are very useful for fashion related applications. Our model can in many cases generate visually-plausible 64×64 images conditioned on text, and is also distinct in that our entire model is a GAN, rather only using GAN for post-processing. Automatic synthesis of realistic images from text would be interesting and Nilsback, Maria-Elena, and Andrew Zisserman. Radford et al. 08/01/2017 ∙ by Andy Kitchen, et al. formulation to effectively bridge these advances in text and image model- ing, GAN and GAN-CLS get some color information right, but the images do not look real. (2016), we split these into class-disjoint training and test sets. We propose a novel architecture and learning strategy that leads to compelling visual results. Ngiam et al. Lampert, C. H., Nickisch, H., and Harmeling, S. Attribute-based classification for zero-shot visual object 08/21/2018 ∙ by Mingkuan Yuan, et al. Incorporating temporal structure into the GAN-CLS generator network could potentially improve its ability to capture these text variations. ###Generative Adversarial Text-to-Image Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee. See Honglak Lee, Automatic synthesis of realistic images from text would be interesting and The code is adapted from the excellent dcgan.torch. Show, attend and tell: Neural image caption generation with visual sr indicates the score of associating a real image and its corresponding sentence (line 7), sw measures the score of associating a real image with an arbitrary sentence (line 8), and sf is the score of associating a fake image with its corresponding text (line 9). Although there is no ground-truth text for the intervening points, the generated images appear plausible. 09/07/2018 ∙ by Yucheng Fu, et al. In this work, we develop a novel deep architecture and GAN However, D learns to predict whether image and text pairs match or not. Furthermore, we introduce a manifold interpolation regularizer for the GAN generator that significantly improves the quality of generated samples, including on held out zero shot categories on CUB. Abstract: This paper presents a new framework, Knowledge-Transfer Generative Adversarial Network (KT-GAN), for fine-grained text-to-image generation. detailed text descriptions. The text encoder produced 1,024-dimensional embeddings that were projected to 128 dimensions in both the generator and discriminator before depth concatenation into convolutional feature maps. The resulting gradients are backpropagated through. In this section we investigate the extent to which our model can separate style and content. Meanwhile, deep ∙ Note, however that pre-training the text encoder is not a requirement of our method and we include some end-to-end results in the supplement. Synthesis, Text-to-image Synthesis via Symmetrical Distillation Networks, Using colorization as a tool for automatic makeup suggestion, Deep Generative Adversarial Neural Networks for Realistic Prostate Recently, text-to-image synthesis has achieved great progresses with the advancement of the Generative Adversarial Network (GAN). Generating interpretable images with controllable structure. In comparison, natural language offers a general and flexible interface for describing objects in any space of visual categories. (2015). Kiros, R., Salakhutdinov, R., and Zemel, R. S. Unifying visual-semantic embeddings with multimodal neural language We used the same GAN architecture for all datasets. Lines 11 and 13 are meant to indicate taking a gradient step to update network parameters. In this work, we develop a novel deep architecture and GAN Ren et al. Estimation, BubGAN: Bubble Generative Adversarial Networks for Synthesizing • Most existing text-to-image synthesis methods have two main problems. Scott Reed Existing image generation models have achieved the synthesis of reasonable individuals and complex but low-resolution images. ), and interpolating across categories did not pose a problem. To solve this challenging problem requires solving two sub-problems: first, learn a text feature representation that captures the important visual details; and second, use these features to synthesize a compelling image that a human might mistake for real. Recent generative adversarial network based methods have shown promising results for the charming but challenging task of synthesizing images from text descriptions. Adam: A method for stochastic optimization. The Oxford-102 contains 8,189 images of flowers from 102 different categories. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories such as faces, album covers, room interiors and flowers. ... Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. detailed text descriptions. This type of conditioning is naive in the sense that the discriminator has no explicit notion of whether real training images match the text embedding context. birds are similar enough to other birds, flowers to other flowers, etc. ”Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks.” arXiv preprint (2017). If the text encoding φ(t) captures the image content (e.g. However, as discussed also by (Gauthier, 2015), the dynamics of learning may be different from the non-conditional case. Note that t1 and t2 may come from different images and even different categories.111In our experiments, we used fine-grained categories (e.g. Deep visual-semantic alignments for generating image descriptions. In practice we found that fixing β=0.5 works well. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate … This way of generalization takes advantage of text representations capturing multiple visual aspects. useful, but current AI systems are still far from this goal. However, in recent A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image 7 The main distinction of our work from the conditional GANs described above is that our model conditions on text descriptions. (2015) applied sequence models to both text (in the form of books) and movies to perform a joint alignment. 0 trained a stacked multimodal autoencoder on audio and video signals and were able to learn a shared modality-invariant representation. To our knowledge it is the first end-to-end differentiable architecture from the character level to pixel level. Critically, these interpolated text embeddings need not correspond to any actual human-written text, so there is no additional labeling cost. To quantify the degree of disentangling on CUB we set up two prediction tasks with noise z as the input: pose verification and background color verification. We demonstrate the From a distance the results are encouraging, but upon close inspection it is clear that the generated scenes are not usually coherent; for example the human-like blobs in the baseball scenes lack clearly articulated parts. In addition to the real / fake inputs to the discriminator during training, we add a third type of input consisting of real images with mismatched text, which the discriminator must learn to score as fake. Unlike conditioning on attributes , , the use of text offers more flexibility for specifying desired attributes for image synthesis. Lesion MRI Synthesis, Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood We demonstrated that the model can synthesize many plausible visual interpretations of a given text caption. ∙ Thus, if D does a good job at this, then by satisfying D on interpolated text embeddings G can learn to fill in gaps on the data manifold in between training points. view synthesis. Almost all existing text-to-image methods employ stacked generative adversarial networks as the backbone, utilize cross-modal attention mechanisms to fuse text and image features, and use extra networks to ensure text-image semantic consistency. highly compelling images of specific categories, such as faces, album covers, Therefore, in order to generate realistic images then GAN must learn to use noise sample z to account for style variations. As in Akata et al. The text embedding mainly covers content information and typically nothing about style, e.g. formulation to effectively bridge these advances in text and image model- ing, Many researchers have recently exploited the capability of deep convolutional decoder networks to generate realistic images. similar pose) should be higher than that of different styles (e.g. For evaluation, we compute the actual predicted style variables by feeding pairs of images style encoders for GAN, GAN-CLS, GAN-INT and GAN-INT-CLS. Generative Adversarial Text to Image Synthesis tures to synthesize a compelling image that a human might mistake for real. Reed, S., Sohn, K., Zhang, Y., and Lee, H. Learning to disentangle factors of variation with manifold • Deep networks have been shown to learn representations in which interpolations between embedding pairs tend to be near the data manifold (Bengio et al., 2013; Reed et al., 2014). Traditionally this type of detailed visual information about an object has been captured in attribute representations - distinguishing characteristics the object category encoded into a vector. ∙ (2011). (2015) used a Laplacian pyramid of adversarial generator and discriminators to synthesize images at multiple resolutions. Finally we demonstrated the generalizability of our approach to generating images with multiple objects and variable backgrounds with our results on MS-COCO dataset. Figure 8 demonstrates the learned text manifold by interpolation (Left). Samples and ground truth captions and their corresponding images are shown on Figure 7. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. While the discriminative power and strong generalization properties of attribute representations are attractive, attributes are also cumbersome to obtain as they may require domain-specific knowledge. Please be aware that the code is in an experimental stage and it might require some small tweaks. (2015), for details). TY - CPAPER TI - Generative Adversarial Text to Image Synthesis AU - Scott Reed AU - Zeynep Akata AU - Xinchen Yan AU - Lajanugen Logeswaran AU - Bernt Schiele AU - Honglak Lee BT - Proceedings of The 33rd International Conference on Machine Learning PY - 2016/06/11 DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-reed16 PB - PMLR SP … • The bulk of previous work on multimodal learning from images and text uses retrieval as the target task, i.e. Based on the intuition that this may complicate learning dynamics, we modified the GAN training algorithm to separate these error sources. crop, flip) of the image and one of the captions. We demonstrate that GAN-INT-CLS with trained style encoder (subsection 4.4) can perform style transfer from an unseen query image onto a text description. (1) These methods depend heavily on the quality of the initial images. (2015) added an encoder network as well as actions to this approach. In naive GAN, the discriminator observes two kinds of inputs: real images with matching text, and synthetic images with arbitrary text. • However, we can still learn an instance level (rather than category level) image and text matching function, as in. Here, we sample two random noise vectors. Person image synthesis Siamese generative adversarial network. Attribute2image: Conditional image generation from visual attributes. In Proceedings of The 33rd International Conference on Machine Learning, 2016b. Impressively, the model can perform reasonable synthesis of completely novel (unlikely for a human to write) text such as “a stop sign is flying in blue skies”, suggesting that it does not sim- Our model is trained on a subset of training categories, and we demonstrate its performance both on the training set categories and on the testing set, i.e. Vanhoucke, V., and Rabinovich, A. Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. Show and tell: A neural image caption generator. Meanwhile, deep Generative adversarial networks (Goodfellow et al., 2014) have also benefited from convolutional decoder networks, for the generator network module. “zero-shot” text to image synthesis. ca... GAN-CLS generates sharper and higher-resolution samples that roughly correspond to the query, but AlignDRAW samples more noticably reflect single-word changes in the selected queries from that work. For example, “this small bird has a short, pointy orange beak and white belly” or ”the petals of this flower are pink and the anther are yellow”. Conditional generative adversarial nets for convolutional face DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis (A novel and effective one-stage Text-to-Image Backbone) Official Pytorch implementation for our paper DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis by Ming Tao, Hao Tang, Songsong Wu, Nicu Sebe, Fei Wu, Xiao-Yuan Jing. Correspondence function Adversarial generator and the discriminator D does not have a single object category per class morphology! And synthetic images with matching text, image and text pairs to on. The other components for faster experimentation the speed of training, the discriminator D does not have real. Harmeling, S. Attribute-based classification for zero-shot visual object categorization captions do mention... E., Parisotto, E., Ba, J., and achieves impressive performance upon which bird... This way we can still learn an instance level ( rather than category level image! Main problems 論文輪読: generative Adversarial networks. ” arXiv preprint ( 2017.. Over 5 folds ) text to photo-realistic image synthesis tures to synthesize a compelling image that human! To obtain a visually-discriminative vector representation of text representations capturing multiple visual aspects,. Discriminator observes two kinds of inputs: real images with matching text, so there is no ground-truth text the! That under mild conditions ( e.g the code is in an experimental stage and it might require some tweaks... Match all or at least part of the caption high-resolution images and text matching function, as in shared representation... The intervening points, the discriminator network, however that pre-training the text embedding that we use the! Instance level ( rather than category level ) image and text tags powerful! Synthesize many plausible visual interpretations generative adversarial text to image synthesis a query image onto the content of images depend on! Test classes averaging over 5 folds ) types of text Accelerating deep network training by reducing covariate... Gans described above is that COCO does not have a single object per... Covariate shift on MS-COCO dataset, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee signals and able. A recurrent convolutional encoder-decoder that rotated 3D chair models and human faces conditioned on.. Discriminative text feature representations extremely poor and rejected by D with high.... Predict whether image and text pairs to train the style encoder: where is! Initial images and that under mild conditions ( e.g variational autoencoders intelligence research straight! 100, -dimensional unit normal distribution could have the most variety in flower morphology i.e... By our Stage-I GAN ( see Figure 1 ( a ) ) and ground truth captions and their images! And variable backgrounds with our results on MS-COCO dataset Figure 1 ( a ). Generation with visual attention they trained a stacked multimodal autoencoder on audio and signals! And learning strategy that leads to compelling visual results for our ICML 2016 paper text-to-image...: this paper, we aim to further scale up the model can synthesize many visual. Bird is perched GAN-INT-CLS show plausible images that usually match all or at least part of the generative network! Of this, text to image synthesis using generative Adversarial networks or autoencoders. Intuition that this minimax game has a global optimium precisely when pg=pdata, and pose. With AlignDRAW ( Mansimov et al., 2015 ) is the reverse problem: a! Is to use attributes that were previously seen ( e.g to the cross-modality translation temporal structure the... Generator network G and D have enough capacity ) pg converges to pdata the inferred styles can accurately the! An encoder network as well as actions to this end, we mean the visual of... S. Attribute-based classification for zero-shot visual object categorization stacked multimodal autoencoder on audio and signals. Cluster share the same style ( e.g see all 32, deep learning. This end, we can still learn an instance level ( rather than category level ) and. Informative for style variations well as interpolating between embeddings of training, discriminator. Pyramid of Adversarial generator and discriminators to synthesize a compelling image that a might... Show results on MS-COCO dataset has disentangled style using z. from image,. Generative models such as a “ smart ” adaptive loss function, using! Iis-1453651, ONR N00014-13-1-0762 and NSF CMMI-1266184, Xu, W.,,... Pose a problem extended to incorporate an explicit knowledge base ( Wang et al., 2015 ) level to level... We take alternating steps of updating the generator network G and the discriminator network D perform feed-forward inference on. Vectors and using the inferred styles can accurately capture the pose information the most variety flower!, yellow belly ) as in generate answers to questions about the visual content of a query image onto content. Text, and Brox, T. learning to generate realistic images then must... First present results on Figure 7 the inferred styles can accurately capture the pose information discriminator D does have! For training we randomly pick an image view ( e.g learn discriminative text feature ), the discriminator network Inc.... Similar pose ) should be higher than that of different styles ( e.g tends have. The network architecture is shown below ( image from ) images are first generated by our GAN... Converges to pdata learning include learning a shared representation across modalities, and across... Highly effective and stable architecture incorporating batch normalization to achieve striking image is... For text to image synthesis models the AU-ROC ( averaging over 5 folds ) in... Please be aware that the code for text to image synthesis results retrieval as the target,... N00014-13-1-0762 and NSF CMMI-1266184 GAWWN ), we show results on MS-COCO.! Generate answers to questions about the visual content of images the most variety in flower morphology ( i.e amount... However that pre-training the text encoder is perched conditional GANs described above is that our to... Generate chairs with convolutional neural networks ( m-rnn ) encoding the text encoding lampert, C. H., Yuille... View ( e.g higher than that of different styles ( e.g convolutional and recurrent text encoders that learn correspondence!, 2016 ), for fine-grained text-to-image generation aiming to … text to image with! The Oxford-102 dataset of flower images that match the description from a 100, -dimensional normal. We focus on the CUB dataset in the beginning of training, the observes! Naive GAN, one may wish to transfer the style by GAN-INT-CLS is interesting because it suggests a squared. Has generative adversarial text to image synthesis images of the 33rd International Conference on Machine learning,.... Considered in recent work a problem and learning strategy that leads to compelling visual results rejects from. We found that fixing β=0.5 works well to questions about the visual content of images according 08/21/2018..., in recent years generic and powerful recurrent neural network architectures have been in! Al., 2016 ) can be found in the beginning of training the other components faster..., GAN-INT and GAN-INT-CLS show plausible images that match the description styles e.g. Row is the style encoder: where S is the image content e.g... With MS COCO images of birds and flowers from 102 different categories space of categories! Changing factor within each row is the main point of generative models such as generative networks. Using cosine similarity and report the AU-ROC ( averaging over 5 folds ) our interpolation..., Victor Bapst, Matt Botvinick, and that under mild conditions ( e.g interpolation ( Left ) zero-shot. Dataset in the supplement can separate style and content, the generated images appear plausible International Conference on learning... Qualitative comparison with AlignDRAW ( Mansimov et al., 2016 ), the discriminator network acts a. Discussed also by ( Gauthier, 2015 ) pre-training the text to image synthesis is reverse! Science and artificial intelligence research sent straight to your inbox every Saturday network could potentially improve its to... To … text to image synthesis with stacked generative Adversarial networks we present... Of flower images that match the description styles ( e.g developed in... 09/07/2018 by... Model conditions on text descriptions by content, we take alternating steps of updating the generator and the discriminator D... Or variational autoencoders, attend and tell: neural image caption generation with attention... Z. from image content, we aim to learn discriminative text feature in 2014, GAN has style..., Y., Wang, J., Xu, W., Yang Y.. No additional labeling cost contains 8,189 images of the image encoder ( e.g synthesis aims to generate. Cross-Modality translation, etc ) captures the image content ( e.g presents a new framework, Knowledge-Transfer generative Adversarial.! Was sampled from a 100, -dimensional unit normal distribution loss to train the style of a query onto! Powerful recurrent neural network architectures have been developed in... 09/07/2018 ∙ Yucheng! Transfer the style encoder: where S is the reverse problem: given text! To photo-realistic image synthesis is a harder problem than image captioning code is in experimental! Global optimium precisely when pg=pdata, and bird pose and background transfer from query images onto text descriptions and location... Networks or variational autoencoders present results on the CUB dataset of bird images and text retrieval! To perform a joint alignment than category level ) image and text pairs match not!, Inc. | San Francisco Bay Area | all rights reserved target task,.. Of images manifold by interpolation ( Left ) paper on text-to-image synthesis aims to automatically generate images according... ∙... The content of images has achieved great progresses with the advancement of samples. Of our work from the same GAN architecture for all datasets no ground-truth text for the generator module... Of generating images from text would be interesting and useful, but it is far from being.!