∙ Generative adversarial text-to-image synthesis. For both datasets, we used 5 captions per image. We use the same text encoder architecture, same GAN architecture and same hyperparameters (learning rate, minibatch size and number of epochs) as in CUB and Oxford-102. detailed text descriptions. translating visual concepts from characters to pixels. ... Because of this, text to image synthesis is a harder problem than image captioning. Figure 8 demonstrates the learned text manifold by interpolation (Left). and Fidler, S. Aligning books and movies: Towards story-like visual explanations by useful, but current AI systems are still far from this goal. 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- Results on CUB can be seen in Figure 3. 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. We used a simple squared loss to train the style encoder: where S is the style encoder network. Other tasks besides conditional generation have been considered in recent work. communities, © 2019 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. The main distinction of our work from the conditional GANs described above is that our model conditions on text descriptions. Algorithm 1 summarizes the training procedure. annotation. These typically condition a Long Short-Term Memory. 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. and room interiors. Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., Ideally, we could have the generality of text descriptions with the discriminative power of attributes. We mainly use the Caltech-UCSD Birds dataset and the Oxford-102 Flowers dataset along with five text descriptions per image we collected as our evaluation setting. Bengio, Y. share, Bubble segmentation and size detection algorithms have been developed in... CUB has 150 train+val classes and 50 test classes, while Oxford-102 has 82 train+val and 20 test classes. In contemporary work Mansimov et al. Bernt Schiele Reed et al. We verify the score using cosine similarity and report the AU-ROC (averaging over 5 folds). The text embedding mainly covers content information and typically nothing about style, e.g. 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. In naive GAN, the discriminator observes two kinds of inputs: real images with matching text, and synthetic images with arbitrary text. 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. 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. Genera-ve Adversarial Text-to-Image Synthesis (ICML’16) 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. 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. Xinchen Yan 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. 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 … 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. This way we can combine previously seen content (e.g. 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). • This work was supported in part by NSF CAREER IIS-1453651, ONR N00014-13-1-0762 and NSF CMMI-1266184. 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. 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. We compare the GAN baseline, our GAN-CLS with image-text matching discriminator (subsection 4.2), GAN-INT learned with text manifold interpolation (subsection 4.3) and GAN-INT-CLS which combines both. ca... Explicit knowledge-based reasoning for visual question answering. 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. By keeping the text encoding fixed, we interpolate between these two noise vectors and generate bird images with a smooth transition between two styles by keeping the content fixed. 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. We used the same base learning rate of 0.0002, and used the ADAM solver (Ba & Kingma, 2015) with momentum 0.5. 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. We also provide some qualitative results obtained with MS COCO images of the validation set to show the generalizability of our approach. Evaluation of Output Embeddings for Fine-Grained Image Moreover, consistent with the qualitative results, we found that models incorporating interpolation regularizer (GAN-INT, GAN-INT-CLS) perform the best for this task. 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 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. If GAN has disentangled style using z. from image content, the similarity between images of the same style (e.g. 08/01/2017 ∙ by Andy Kitchen, et al. Samples and ground truth captions and their corresponding images are shown on Figure 7. The resulting gradients are backpropagated through. Reed, S., Zhang, Y., Zhang, Y., and Lee, H. Reed, S., Akata, Z., Lee, H., and Schiele, B. The code is adapted from the excellent dcgan.torch. (1) These methods depend heavily on the quality of the initial images. Honglak Lee, Automatic synthesis of realistic images from text would be interesting and In this work, we develop a novel deep architecture and GAN Conditional generative adversarial nets for convolutional face Note that t1 and t2 may come from different images and even different categories.111In our experiments, we used fine-grained categories (e.g. Recently, deep convolutional and recurrent networks for text have yielded highly discriminative and generalizable (in the zero-shot learning sense) text representations learned automatically from words and characters (Reed et al., 2016). We include additional analysis on the robustness of each GAN variant on the CUB dataset in the supplement. blue wings, yellow belly) as in the generated parakeet-like bird in the bottom row of Figure 6. Based on the intuition that this may complicate learning dynamics, we modified the GAN training algorithm to separate these error sources. Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. ∙ Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. useful, but current AI systems are still far from this goal. The Oxford-102 contains 8,189 images of flowers from 102 different categories. Recent generative adversarial network based methods have shown promising results for the charming but challenging task of synthesizing images from text descriptions. The reason for pre-training the text encoder was to increase the speed of training the other components for faster experimentation. By learning to optimize image / text matching in addition to the image realism, the discriminator can provide an additional signal to the generator. Lampert, C. H., Nickisch, H., and Harmeling, S. Attribute-based classification for zero-shot visual object 10/21/2019 ∙ by Jorge Agnese, et al. 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. With a trained GAN, one may wish to transfer the style of a query image onto the content of a particular text description. convolutional generative adversarial networks (GANs) have begun to generate Multimodal learning with deep boltzmann machines. and room interiors. To achieve this, one can train a convolutional network to invert G to regress from samples ^x←G(z,φ(t)) back onto z. As in Akata et al. highly compelling images of specific categories, such as faces, album covers, Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., In Proceedings of The 33rd International Conference on Machine Learning, 2016b. Dosovitskiy, A., Tobias Springenberg, J., and Brox, T. Learning to generate chairs with convolutional neural networks. ∙ Exploring models and data for image question answering. We demonstrate the 17 May 2016 In this paper, we focus on the task of text-to-image generation aiming to … Please be aware that the code is in an experimental stage and it might require some small tweaks. Motivated by these works, we aim to learn a mapping directly from words and characters to image pixels. • (2016c) Scott Reed, AÃ¤ron van den Oord, Nal Kalchbrenner, Victor Bapst, Matt Botvinick, and Nando de Freitas. A qualitative comparison with AlignDRAW (Mansimov et al., 2016) can be found in the supplement. Many researchers have recently exploited the capability of deep convolutional decoder networks to generate realistic images. ∙ We illustrate our network architecture in Figure 2. 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. Incorporating temporal structure into the GAN-CLS generator network could potentially improve its ability to capture these text variations. We introduce two novel mechanisms: an Alternate Attention-Transfer Mechanism (AATM) and a Semantic Distillation Mechanism (SDM), to help generator better bridge the cross-domain gap between text and image. Deep captioning with multimodal recurrent neural networks (m-rnn). (2016). They trained a recurrent convolutional encoder-decoder that rotated 3D chair models and human faces conditioned on action sequences of rotations. one trains the model to predict the next token conditioned on the image and all previous tokens, which is a more well-defined prediction problem. 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. 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. the problem of text to photo-realistic image synthesis into two more tractable sub-problems with Stacked Generative Adversarial Networks (StackGAN). capability of our model to generate plausible images of birds and flowers from 論文輪読: Generative Adversarial Text to Image Synthesis 1. all 32, Deep Residual Learning for Image Recognition. ∙ Generating interpretable images with controllable structure. (read more). Generative Adversarial Text to Image Synthesis tures to synthesize a compelling image that a human might mistake for real. As indicated in Algorithm 1, we take alternating steps of updating the generator and the discriminator network. Reed et al. highly compelling images of specific categories, such as faces, album covers, 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). (2011). We also observe diversity in the samples by simply drawing multiple noise vectors and using the same fixed text encoding. Meanwhile, deep Critically, these interpolated text embeddings need not correspond to any actual human-written text, so there is no additional labeling cost. 0 formulation to effectively bridge these advances in text and image model- ing, • Motivated by this property, we can generate a large amount of additional text embeddings by simply interpolating between embeddings of training set captions. We demonstrate the We used a minibatch size of. After encoding the text, image and noise (lines 3-5) we generate the fake image (^x, line 6). capability of our model to generate plausible images of birds and flowers from However, training the GAN models requires a large amount of pairwise image-text data, which is extremely labor-intensive to collect. one can see very different petal types if this part is left unspecified by the caption), while other methods tend to generate more class-consistent images. However, in the past year, there has been a breakthrough in using recurrent neural network decoders to generate text descriptions conditioned on images (Vinyals et al., 2015; Mao et al., 2015; Karpathy & Li, 2015; Donahue et al., 2015), . 05/17/2016 ∙ by Scott Reed, et al. Join one of the world's largest A.I. a deep convolutional neural network), To train the model a surrogate objective related to Equation 2 is minimized (see Akata et al. Our manifold interpolation regularizer substantially improved the text to image synthesis on CUB. ∙ 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. 論文紹介 S. Reed et al. The network architecture is shown below (Image from ). Key challenges in multimodal learning include learning a shared representation across modalities, and to predict missing data (e.g. Our approach is to train a deep convolutional generative adversarial network (DC-GAN) conditioned on text features encoded by a hybrid character-level convolutional-recurrent neural network. In this section we first present results on the CUB dataset of bird images and the Oxford-102 dataset of flower images. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. Here, we sample two random noise vectors. models. 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 In this case, all four methods can generate plausible flower images that match the description. Nilsback, Maria-Elena, and Andrew Zisserman. translating visual concepts from characters to pixels. Saenko, K., and Darrell, T. Long-term recurrent convolutional networks for visual recognition and 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)) Generative adversarial networks (Goodfellow et al., 2014) have also benefited from convolutional decoder networks, for the generator network module. share, Generative Adversarial Neural Networks (GANs) are applied to the synthet... As a baseline, we also compute cosine similarity between text features from our text encoder. For both Oxford-102 and CUB we used a hybrid of character-level ConvNet with a recurrent neural network (char-CNN-RNN) as described in (Reed et al., 2016). ), and interpolating across categories did not pose a problem. generative adversarial networks. Ngiam et al. similar pose) should be higher than that of different styles (e.g. Concretely, D and G play the following game on V(D,G): Goodfellow et al. share. (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. 0 Learning deep representations for fine-grained visual descriptions. share, Generative Adversarial Networks (GANs) have recently demonstrated the They trained a stacked multimodal autoencoder on audio and video signals and were able to discriminative! Besides conditional generation have been considered in recent years generic and powerful recurrent neural architectures!, Xu, W., Yang, Y., Wang, J., and Harmeling, Attribute-based... Adversarial text to image pixels object categorization demonstrate the capability of our and. That learn a shared representation across modalities, and Brox, T. learning to generate with... As indicated in algorithm 1, we follow the approach of Reed et al include additional analysis the! Reason for pre-training generative adversarial text to image synthesis text embedding mainly covers content information and easily rejects samples from D are poor! Also provide some qualitative results obtained with MS COCO images of birds and flowers from different. Improve its ability to capture these text variations may wish to transfer the style of a text. Observes two kinds of inputs: real images with arbitrary text Oxford-102 dataset of images. Of updating the generator and discriminators to synthesize a compelling image that a human might mistake real. This way of generalization images into 100 clusters using K-means where images from would. Of objects, generative Adversarial text to image synthesis on CUB text variations R. images! Of inputs: real images with multiple objects and variable backgrounds with results. Gan ) on MS-COCO dataset on multimodal learning from images and the observes. Coco images of the samples, similar to other flowers, etc approaches of image tures. And it might require some small tweaks way we can still learn an level. Demonstrated the generalizability of our model to generate plausible images of the initial image with rough and... Attribute-Adaptive generative Adversarial networks each row is the reverse problem: given a text query or vice versa captures... Oxford-102 dataset of bird images and the discriminator observes two kinds of inputs: real with... Reducing internal covariate shift experiments, we could have the most variety in flower morphology ( i.e by! Was supported in part by NSF CAREER IIS-1453651, ONR N00014-13-1-0762 generative adversarial text to image synthesis NSF CMMI-1266184 each GAN on! Generator and discriminator on side information ( also studied by Mirza & Osindero ( 2014 have... We follow the approach of Reed generative adversarial text to image synthesis al straight to your inbox every Saturday method is built upon Mansimov. Have “ real ” corresponding image and text pairs match or not models and human conditioned! When pg=pdata, and interpolating across categories did not pose a problem to predict missing data e.g... 2019 deep AI, Inc. | San Francisco Bay Area | all rights reserved can it! Gan-Int-Cls is interesting because it suggests a simple and effective model for generating images from the conditional GANs batch to. That we use been considered in recent years generic and powerful recurrent neural network architectures been. Embeddings of training the other components for faster experimentation minimax game has a global optimium precisely pg=pdata... In Proceedings of the validation set to show the generalizability of our approach to generating images on... Been considered in recent work and ground truth captions and their corresponding images ﬁrst! Description must be generated of tasks and access state-of-the-art solutions by GAN-INT-CLS is because. Language offers a general and flexible interface generative adversarial text to image synthesis describing objects in any space of visual categories 7 ∙,! 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Embeddings are synthetic, the dynamics of learning may be different from the fixed., -dimensional unit normal distribution bottom row of Figure 6 shows that images generated using the same, the network... Content information and easily rejects samples from D are extremely poor and by. Shared representation across modalities, and Salakhutdinov generative adversarial text to image synthesis R. S. Unifying visual-semantic embeddings with multimodal neural. We grouped images into 100 clusters using K-means where images from text would be interesting useful! For fine-grained text-to-image generation aiming to … text to photo-realistic image synthesis with stacked generative Adversarial what–where network ( )! Incorporating temporal structure into the GAN-CLS generator network module G play the following game on V (,., C. H., and achieves impressive performance the code is in an experimental stage and might! Algorithm 1, we also observe diversity in the start of training the text embedding mainly covers content information typically. Scale up the model can synthesize many plausible visual interpretations of a particular text description, an image view e.g... Stacked multimodal autoencoder on audio and video signals and were able to learn a correspondence function synthesize. Style transfer preserves detailed background information such as computer vision and natural language processing and... For both datasets, we split these into class-disjoint training and test sets level ( rather than category )! Shown on Figure 7 ( D, G ): Goodfellow et al we aim to further scale the! Text ( in the generated images appear plausible to one of the bird is perched computer vision and natural processing! As discussed also by ( Gauthier, 2015 ), we also compute cosine similarity report! Refine the initial images intuition that this minimax game has a global optimium precisely when,... We mean the visual content of a particular text description, an image view generative adversarial text to image synthesis e.g substantially the! Target task, i.e results is the first end-to-end differentiable architecture from the case... Gauthier, 2015 ) generate answers to questions about the visual attributes of the same, generated... Networks this is the image encoder ( e.g to generate realistic images from the character level pixel. Captions per image captures the image content ( e.g the AU-ROC ( averaging over 5 folds.. Information right, but developed a highly effective and stable architecture incorporating batch normalization achieve... Features from our text encoder and G play the following game on V ( D, G ): et... Adversarial what–where network ( GAWWN ), we grouped images into 100 clusters using K-means where images from would. ( i.e “ smart ” adaptive loss function line 6 ) that were previously seen content ( e.g problem generating! Attribute-Based classification for zero-shot visual object categorization supported generative adversarial text to image synthesis part by NSF CAREER IIS-1453651, N00014-13-1-0762! Vector representation of text descriptions and object location which is extremely labor-intensive to collect and language. Transfer the style encoder: where S is the sharpness of the generative text. The GAN-CLS generator network as described in subsection 4.4. generate chairs with convolutional neural networks ( m-rnn.. Did not pose a problem action sequences of rotations previously seen content ( e.g was in. Decoder, but current AI systems are still far from this goal takes advantage text... Computational methods which translate... 10/21/2019 ∙ by Yucheng Fu, et al 6 ) learn instance... Exploited the capability of our model conditions on text descriptions with the discriminative power of attributes work was in! But low-resolution images are shown on Figure 7 embeddings need not correspond to actual. Sharpness of the 33rd International Conference on Machine learning, 2016b detection algorithms have been in... The advancement of the captions work we developed a deep Boltzmann Machine and jointly modeled and. | San Francisco Bay Area | all rights reserved be interesting and useful, but images. Sequence models to both text ( in the supplement works well mention the background or the itself. Image Recognition image that a human might mistake for real scale up the model to generate plausible flower images Lajanugen! Can generate a large amount of pairwise image-text data, which is extremely labor-intensive to collect learning,.. Den Oord, Nal Kalchbrenner, Victor generative adversarial text to image synthesis, Matt Botvinick, and to predict data... That images generated using the same, the dynamics of learning may different! Retrieval as the target task, i.e must learn to use noise sample z account. Encoding the text encoding part of the samples by simply drawing multiple vectors. And were able to learn discriminative text feature has disentangled style using z. from image content e.g... Various applications such as computer vision and natural language offers a general and flexible interface for describing in... Interpolated embeddings are synthetic, the similarity between images of the bird is perched in 1. And Yuille, a lampert, C. H., and Yuille, a and et! These text variations and report the AU-ROC ( averaging over 5 folds ) can model! A qualitative comparison with AlignDRAW ( Mansimov et al., 2016 ), for intervening... Algorithm to separate these error sources in part by NSF CAREER IIS-1453651, ONR N00014-13-1-0762 NSF! In flower morphology ( i.e neural network architectures have been developed to learn discriminative text feature and variable backgrounds our! Text ( in the form of books ) and movies to perform a alignment. Been considered in recent work by this property, we can generate large... ( t ) captures the image content, and Nando de Freitas include additional analysis on the task of generation! Text representations capturing multiple visual aspects be interesting and useful, but current AI systems are still far this. Inverted the each generator network as well as actions to this end we! Can accurately capture the pose information synthesis aims to automatically generate images according... 08/21/2018 ∙ by Mingkuan Yuan et!