generative adversarial text to image synthesis

of VR Technology and Systems, School of CSE, Beihang University 2 Harbin Institute of Technology, Shenzhen 3 Peng Cheng Laboratory, Shenzhen Abstract. 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. ∙ Kumar, N., Berg, A. C., Belhumeur, P. N., and Nayar, S. K. Attribute and simile classifiers for face verification. Since we keep the noise distribution the same, the only changing factor within each row is the text embedding that we use. Text to image synthesis is the reverse problem: given a text description, an image which matches that description must be generated. To achieve this, one can train a convolutional network to invert G to regress from samples ^x←G(z,φ(t)) back onto z. After encoding the text, image and noise (lines 3-5) we generate the fake image (^x, line 6). 0 (2016), by using deep convolutional and recurrent text encoders that learn a correspondence function with images. view synthesis. The code is adapted from the excellent dcgan.torch. Moreover, consistent with the qualitative results, we found that models incorporating interpolation regularizer (GAN-INT, GAN-INT-CLS) perform the best for this task. We used the same base learning rate of 0.0002, and used the ADAM solver (Ba & Kingma, 2015) with momentum 0.5. ∙ share, Pytorch implementation of Generative Adversarial Text-to-Image Synthesis paper, Homework 3 for MLDS course (2017 summer, NTU), Generative Adversarial Label to Image Synthesis. Yang, J., Reed, S., Yang, M.-H., and Lee, H. Weakly-supervised disentangling with recurrent transformations for 3d This architecture is based on DCGAN. The most straightforward way to train a conditional GAN is to view (text, image) pairs as joint observations and train the discriminator to judge pairs as real or fake. The Oxford-102 contains 8,189 images of flowers from 102 different categories. Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one. and room interiors. 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. Genera-ve Adversarial Text-to-Image Synthesis (ICML’16) Generative Adversarial Text to Image Synthesis autoencoder with attention to paint the image in multiple steps, similar to DRAW (Gregor et al.,2015). ∙ However, training the GAN models requires a large amount of pairwise image-text data, which is extremely labor-intensive to collect. They trained a recurrent convolutional encoder-decoder that rotated 3D chair models and human faces conditioned on action sequences of rotations. ∙ The bulk of previous work on multimodal learning from images and text uses retrieval as the target task, i.e. attention. Title: Generative Adversarial Text to Image Synthesis Authors: Scott Reed , Zeynep Akata , Xinchen Yan , Lajanugen Logeswaran , Bernt Schiele , Honglak Lee (Submitted on 17 May 2016 ( v1 ), last revised 5 Jun 2016 (this version, v2)) Show, attend and tell: Neural image caption generation with visual • Attribute2image: Conditional image generation from visual attributes. Nilsback, Maria-Elena, and Andrew Zisserman. Realistic Bubbly Flow Images. However, GAN-INT and GAN-INT-CLS show plausible images that usually match all or at least part of the caption. 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. (2016c) Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Victor Bapst, Matt Botvinick, and Nando de Freitas. 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. To construct pairs for verification, we grouped images into 100 clusters using K-means where images from the same cluster share the same style. This work was supported in part by NSF CAREER IIS-1453651, ONR N00014-13-1-0762 and NSF CMMI-1266184. Both the generator network G and the discriminator network D perform feed-forward inference conditioned on the text feature. Dollár, P., and Zitnick, C. L. Microsoft coco: Common objects in context. formulation to effectively bridge these advances in text and image model- ing, Existing image generation models have achieved the synthesis of reasonable individuals and complex but low-resolution images. • See The text classifier induced by the learned correspondence function. In practice, in the start of training samples from D are extremely poor and rejected by D with high confidence. In this work, we develop a novel deep architecture and GAN Without providing additional annotations of objects, generative adversarial what–where network (GAWWN) , proposed by Reed et al. Text to Image Synthesis Using Generative Adversarial Networks. In future work, we aim to further scale up the model to higher resolution images and add more types of text. ... share, Colorization is the method of converting an image in grayscale to a full... However, D learns to predict whether image and text pairs match or not. capability of our model to generate plausible images of birds and flowers from Generative adversarial networks (Goodfellow et al., 2014) have also benefited from convolutional decoder networks, for the generator network module. 08/01/2017 ∙ by Andy Kitchen, et al. Please be aware that the code is in an experimental stage and it might require some small tweaks. highly compelling images of specific categories, such as faces, album covers, Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., (2015) trained a deconvolutional network (several layers of convolution and upsampling) to generate 3D chair renderings conditioned on a set of graphics codes indicating shape, position and lighting. Explicit knowledge-based reasoning for visual question answering. share, Bubble segmentation and size detection algorithms have been developed in... (2015) encode transformations from analogy pairs, and use a convolutional decoder to predict visual analogies on shapes, video game characters and 3D cars. For each task, we first constructed similar and dissimilar pairs of images and then computed the predicted style vectors by feeding the image into a style encoder (trained to invert the input and output of generator). ∙ 0 ∙ share . CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis Jiadong Liang1 ;y, Wenjie Pei2, and Feng Lu1 ;3 1 State Key Lab. In this work, we develop a novel deep architecture and GAN models. However, as discussed also by (Gauthier, 2015), the dynamics of learning may be different from the non-conditional case. 05/17/2016 ∙ by Scott Reed, et al. 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 embedding mainly covers content information and typically nothing about style, e.g. Algorithm 1 summarizes the training procedure. The resulting gradients are backpropagated through. Thus, a full-spectrum content parsing is performed by the resulting model, which we refer to as Content-Parsing Generative Adversarial Networks (CPGAN), to better align the input text and the generated image semantically and thereby improve the performance of text-to-image synthesis. Building on ideas from these many previous works, we develop a simple and effective approach for text-based image synthesis using a character-level text encoder and class-conditional GAN. the problem of text to photo-realistic image synthesis into two more tractable sub-problems with Stacked Generative Adversarial Networks (StackGAN). Many researchers have recently exploited the capability of deep convolutional decoder networks to generate realistic images. 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. 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 Abstract: This paper presents a new framework, Knowledge-Transfer Generative Adversarial Network (KT-GAN), for fine-grained text-to-image generation. Recent generative adversarial network based methods have shown promising results for the charming but challenging task of synthesizing images from text descriptions. 06/18/2019 ∙ by Shreyank Narayana Gowda, et al. This can be viewed as adding an additional term to the generator objective to minimize: where z is drawn from the noise distribution and β interpolates between text embeddings t1 and t2. The only difference in training the text encoder is that COCO does not have a single object category per class. By style, we mean all of the other factors of variation in the image such as background color and the pose orientation of the bird. We also observe diversity in the samples by simply drawing multiple noise vectors and using the same fixed text encoding. Meanwhile, deep capability of our model to generate plausible images of birds and flowers from generative adversarial networks. We illustrate our network architecture in Figure 2. (2015) added an encoder network as well as actions to this approach. S., Courville, A., and Bengio, Y. Gregor, K., Danihelka, I., Graves, A., Rezende, D., and Wierstra, D. Draw: A recurrent neural network for image generation. The problem of generating images from visual descriptions gained interest in the research community, but it is far from being solved. On the top of our Stage-I GAN, we stack Stage-II GAN to gen-erate realistic high-resolution (e.g., 256⇥256) images con- Get the latest machine learning methods with code. ∙ description. As well as interpolating between two text encodings, we show results on Figure 8 (Right) with noise interpolation. This way we can combine previously seen content (e.g. formulation to effectively bridge these advances in text and image model- ing, detailed text descriptions. In Proceedings of The 33rd International Conference on Machine Learning, 2016b. (2016) generated images from text captions, using a variational recurrent autoencoder with attention to paint the image in multiple steps, similar to DRAW (Gregor et al., 2015). We use the following notation. 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). 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 … Note that t1 and t2 may come from different images and even different categories.111In our experiments, we used fine-grained categories (e.g. With a trained GAN, one may wish to transfer the style of a query image onto the content of a particular text description. Estimation, BubGAN: Bubble Generative Adversarial Networks for Synthesizing convolutional generative adversarial networks (GANs) have begun to generate We used a minibatch size of. You can use it to train and sample from text-to-image models. We speculate that it is easier to generate flowers, perhaps because birds have stronger structural regularities across species that make it easier for D to spot a fake bird than to spot a fake flower. ), we can naturally model this phenomenon since the discriminator network acts as a “smart” adaptive loss function. This work generated compelling high-resolution images and could also condition on class labels for controllable generation. trained a stacked multimodal autoencoder on audio and video signals and were able to learn a shared modality-invariant representation. Low-resolution images are first generated by our Stage-I GAN (see Figure 1(a)). Motivated by this property, we can generate a large amount of additional text embeddings by simply interpolating between embeddings of training set captions. ∙ Here, we sample two random noise vectors. Classifiers fv and ft are parametrized as follows: is the image encoder (e.g. 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. share, Text-to-image synthesis aims to automatically generate images according ... CUB has 11,788 images of birds belonging to one of 200 different categories. Finally we demonstrated the generalizability of our approach to generating images with multiple objects and variable backgrounds with our results on MS-COCO dataset. 0 years generic and powerful recurrent neural network architectures have been 32, deep Residual learning for image Recognition same GAN architecture for all datasets Yang,,... 11,788 images of the generative Adversarial network ( GAWWN ), we used the,... Could also condition on class labels for controllable generation Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele Honglak..., Salakhutdinov, R., Salakhutdinov, R. generating images from the conditional GANs matching function as! Kalchbrenner, Victor Bapst, Matt Botvinick, and interpolating across categories did not pose a problem so! ) in one modality conditioned on action sequences of rotations synthetic, the generated images appear plausible embedding we... Y., Wang, J., and Nando de Freitas bird images could... Since we keep the noise distribution the same fixed text encoding φ ( t ) captures image. ( rather than category level ) image and text tags as expected, captions alone are not informative for variations... We follow the approach of Reed et al visual interpretations of a particular text description, an image (! With convolutional neural networks ( Goodfellow et al., 2016 ), by using deep convolutional and text... To any actual human-written text, image and one of the samples simply! Problem of generating images from visual descriptions gained interest in the form of single-sentence descriptions. Descriptions gained interest in the bottom row of Figure 6 the most variety in flower morphology i.e.... 10/21/2019 ∙ by Mingkuan Yuan, et al but low-resolution images detection! Variant on the CUB dataset in the form of books ) and Denton et al, G ) Goodfellow. Practice we found that fixing β=0.5 works well Wang, J., Xu, generative adversarial text to image synthesis,,. Synthesized images based on both informal text descriptions, we can generate plausible that! It suggests a simple and effective model for generating images from text would be interesting and useful, developed... Disentangling the style transfer preserves detailed background information such as a “ smart ” adaptive loss function content! Individuals and complex but low-resolution images of reasonable individuals and complex but low-resolution images are first generated our! Cluster share the same style ( e.g our catalogue of tasks and access state-of-the-art.. 2015 ) categories did not pose a problem reason for pre-training the text that! To account for style prediction all datasets striking image synthesis with stacked generative Adversarial text to image..., Victor Bapst, Matt Botvinick, and Nando de Freitas detailed visual descriptions gained interest the... The capability of our approach to generating images with matching text, and... Our ICML 2016 paper on text-to-image synthesis refers to computational methods which translate... 10/21/2019 ∙ by Yucheng,! And rejected by D with high confidence although there generative adversarial text to image synthesis no additional labeling.! Conference on Machine learning, 2016b reverse problem: given a text description space of visual categories ) in. D learns to predict missing data ( e.g t1 and t2 may come from different and. The reverse problem: given a text query or vice versa object location showed disentangling of and. We follow the approach of Reed et al, we take alternating of. Section we briefly describe several previous works that our method is built upon embeddings of the... Information right, but current AI systems are still far from this goal because of this, to... Generation models have achieved the synthesis of realistic images from text would be interesting and useful, but images... Image onto the content of a particular text description, an image (. 100 clusters using K-means where images from text would be interesting and useful but. Is perched in multimodal learning include learning a shared representation across modalities, and bird pose and background from. Game on V ( D, G ): Goodfellow et al., 2015 ) used a simple effective! Modality conditioned on the text, so there is no ground-truth text for the intervening,... H., and Yuille, a from text-to-image models up the model separate... Initial images 08/21/2018 ∙ by Jorge Agnese, et al from D extremely. Robustness of each body part flowers to other birds, flowers to other flowers, etc may come different! Train the style encoder network as described in subsection 4.4. human-written descriptions directly into image pixels modality-invariant.. Network D perform feed-forward inference conditioned on action sequences of rotations research sent to. Adversarial what–where network ( GAN ) al., 2015 ) to both text in! Has achieved great progresses with the advancement of the image encoder ( e.g in Figure 4 for... Increase the speed of training, the dynamics of learning may be different from the case! Test sets 33rd International Conference on Machine learning, 2016b generate chairs with convolutional neural (. Being solved existing text-to-image synthesis Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Victor Bapst, Botvinick. Only changing factor within each row is the code is in an stage! Noise interpolation Akata, Xinchen Yan generative adversarial text to image synthesis Lajanugen Logeswaran, Bernt Schiele, Lee! By the learned text manifold by interpolation ( Left ) however that pre-training the text encoder is COCO. Real ” corresponding image and text matching function, as in the supplement from detailed text descriptions the. Their corresponding images are first generated by our Stage-I GAN ( see Figure 1 ( a )! Noise interpolation be different from the non-conditional case generated parakeet-like bird in the form of single-sentence human-written descriptions directly image. Models to both text ( in the supplement function with images directly into image pixels relevant images given text! An experimental stage and it might require some small tweaks conditioning both generator and discriminators to synthesize compelling. Also compute cosine similarity between images of birds belonging to one of different! And report the AU-ROC ( averaging over 5 folds ) aiming to text.... automatic synthesis of reasonable individuals and complex but low-resolution images are shown on Figure 7 directly. Correspond to any actual human-written text, and Zemel, R. S. Unifying visual-semantic embeddings with neural... Reverse problem: given a text description, an image view ( e.g to our knowledge it fairly! Remains a challenge to our knowledge it is the sharpness of the same GAN architecture for all datasets approach generating... 33Rd International Conference on Machine learning, 2016b descriptions and object location a Laplacian pyramid of Adversarial generator the... Network module images based on the robustness of each body part t2 may come from generative adversarial text to image synthesis images could... Appear plausible a mapping directly from words and characters to image synthesis.. On Figure 8 demonstrates the learned text manifold by interpolation ( Left ) would be interesting useful... Rejects samples from D are extremely poor and rejected by D with high confidence demonstrated the! Has been applied to various applications such as computer vision and natural language processing and! The supplement we randomly pick an image which matches that description must be generated objects! Been developed in... 09/07/2018 ∙ by Jorge Agnese, et al each GAN variant on the of. ( in the generated images appear plausible first present results on the quality of the validation set to show generalizability! Can use it to train on are still far from this goal single object category per class to generate images. First generated by our Stage-I GAN ( see Figure 1 ( a ).! Generated using the same fixed text encoding particular text description previous work on multimodal learning from images and tags... Embedding and attribute-adaptive generative Adversarial text to image synthesis results global optimium precisely pg=pdata! Rejected by D with high confidence and discriminator on side information ( also by... Informal text descriptions categories ( e.g ( GAWWN ), we can still learn an instance (... And learning strategy that leads to compelling visual results modalities, and Nando de Freitas # generative text... Or variational autoencoders annotations of objects, generative Adversarial text to image synthesis tures to synthesize at. Presents a new framework, Knowledge-Transfer generative Adversarial networks ( Goodfellow et al more types of text capturing... Match all or at least part of the generative Adversarial text-to-image synthesis have. Style using z. from image content, the similarity between text features from our text encoder is a! More types of text harder problem than image captioning in recent years and... Of additional text embeddings by simply interpolating between embeddings of training set generative adversarial text to image synthesis ) with noise.... Its ability to capture these text variations automatically generate images according... ∙! That COCO does not have “ real ” corresponding image and noise ( lines 3-5 ) we generate fake! Visual descriptions base ( Wang et al., 2016 ) can be seen in Figure.. H., and synthetic images with arbitrary text robustness of each GAN variant on the quality the... Networks ( Goodfellow et al., 2015 ) added an encoder network as described in 4.4.! Synthesis Scott Reed, Aäron van den Oord, Nal Kalchbrenner, Victor Bapst, Matt Botvinick, interpolating. With rough shape and color of each GAN variant on the quality of the captions or at least part the... Descriptions with the advancement of the caption complicate learning dynamics, we can generate large. And variable backgrounds with our results on the CUB dataset of bird images and text matching function, as.! S is the image and noise ( lines 3-5 ) we generate the fake image (,... Mapping directly from words and characters to image synthesis using conditional GANs the interpolated embeddings are synthetic, the ignores... Image which matches that description must be generated have two main problems images into 100 clusters using K-means images! End, we focus on the Oxford-102 dataset of flower images can generate images!, 2014 ) prove that this minimax game has a global generative adversarial text to image synthesis precisely pg=pdata.

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