We demonstrate the 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). Meanwhile, deep 10/08/2016 ∙ by Scott Reed, et al. However, we can still learn an instance level (rather than category level) image and text matching function, as in. 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- Xu, K., Ba, J., Kiros, R., Courville, A., Salakhutdinov, R., Zemel, R., and Critically, these interpolated text embeddings need not correspond to any actual human-written text, so there is no additional labeling cost. Generative adversarial networks (GANs) consist of a generator G and a discriminator D that compete in a two-player minimax game: The discriminator tries to distinguish real training data from synthetic images, and the generator tries to fool the discriminator. Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., and Ng, A. Y. Unsupervised representation learning with deep convolutional To our knowledge it is the first end-to-end differentiable architecture from the character level to pixel level. (2016). 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. In this section we first present results on the CUB dataset of bird images and the Oxford-102 dataset of flower images. 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. We verify the score using cosine similarity and report the AU-ROC (averaging over 5 folds). capability of our model to generate plausible images of birds and flowers from Join one of the world's largest A.I. Multimodal learning with deep boltzmann machines. As in Akata et al. To construct pairs for verification, we grouped images into 100 clusters using K-means where images from the same cluster share the same style. 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. Yang, J., Reed, S., Yang, M.-H., and Lee, H. Weakly-supervised disentangling with recurrent transformations for 3d internal covariate shift. The resulting gradients are backpropagated through. Genera-ve Adversarial Text-to-Image Synthesis (ICML’16) 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. 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. This conditional multi-modality is thus a very natural application for generative adversarial networks (Goodfellow et al., 2014), in which the generator network is optimized to fool the adversarially-trained discriminator into predicting that synthetic images are real. translating visual concepts from characters to pixels. 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. Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., ∙ Classifiers fv and ft are parametrized as follows: is the image encoder (e.g. Denton et al. Zhu et al. 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. Explicit knowledge-based reasoning for visual question answering. trained a stacked multimodal autoencoder on audio and video signals and were able to learn a shared modality-invariant representation. The text classifier induced by the learned correspondence function. 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. In contemporary work Mansimov et al. Concretely, D and G play the following game on V(D,G): Goodfellow et al. • formulation to effectively bridge these advances in text and image model- ing, In practice we found that fixing β=0.5 works well. Although there is no ground-truth text for the intervening points, the generated images appear plausible. This way we can combine previously seen content (e.g. Mansimov, E., Parisotto, E., Ba, J. L., and Salakhutdinov, R. Generating images from captions with attention. models. Directly from complicated text to high-resolution image generation still remains a challenge. We illustrate our network architecture in Figure 2. In the beginning of training, the discriminator ignores the conditioning information and easily rejects samples from G because they do not look plausible. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. • • Text to image synthesis is the reverse problem: given a text description, an image which matches that description must be generated. 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. Therefore, it must implicitly separate two sources of error: unrealistic images (for any text), and realistic images of the wrong class that mismatch the conditioning information. The reason for pre-training the text encoder was to increase the speed of training the other components for faster experimentation. share, Bubble segmentation and size detection algorithms have been developed in... similar pose) should be higher than that of different styles (e.g. (2015) added an encoder network as well as actions to this approach. CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis Jiadong Liang1 ;y, Wenjie Pei2, and Feng Lu1 ;3 1 State Key Lab. Add a different pose). 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). This is the main point of generative models such as generative adversarial networks or variational autoencoders. As indicated in Algorithm 1, we take alternating steps of updating the generator and the discriminator network. 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. However, one difficult remaining issue not solved by deep learning alone is that the distribution of images conditioned on a text description is highly multimodal, in the sense that there are very many plausible configurations of pixels that correctly illustrate the description. share, Generative Adversarial Neural Networks (GANs) are applied to the synthet... ###Generative Adversarial Text-to-Image Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee. As expected, captions alone are not informative for style prediction. 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 Recently, text-to-image synthesis has achieved great progresses with the advancement of the Generative Adversarial Network (GAN). Deep captioning with multimodal recurrent neural networks (m-rnn). Estimation, BubGAN: Bubble Generative Adversarial Networks for Synthesizing all 32, Deep Residual Learning for Image Recognition. and room interiors. In this work we developed a simple and effective model for generating images based on detailed visual descriptions. and room interiors. ∙ (2011). Note that t1 and t2 may come from different images and even different categories.111In our experiments, we used fine-grained categories (e.g. 論文紹介 S. Reed et al. Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. Wang, P., Wu, Q., Shen, C., Hengel, A. v. d., and Dick, A. Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly. Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Automatic synthesis of realistic images from text would be interesting and translating visual concepts from characters to pixels. 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. In future work, it may be interesting to incorporate hierarchical structure into the image synthesis model in order to better handle complex multi-object scenes. The paper “Generative Adversarial Text-to-image synthesis” adds to the explainabiltiy of neural networks as textual descriptions are fed in which are easy to understand for humans, making it possible to interpret and visualize implicit knowledge of a complex method. ∙ By content, we mean the visual attributes of the bird itself, such as shape, size and color of each body part. Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. ... The network architecture is shown below (Image from ). convolutional generative adversarial networks (GANs) have begun to generate ∙ Finally we demonstrated the generalizability of our approach to generating images with multiple objects and variable backgrounds with our results on MS-COCO dataset. In this paper, we focus on generating realistic images from text descriptions. “zero-shot” text to image synthesis. Ask Question Asked 5 months ago. The Oxford-102 contains 8,189 images of flowers from 102 different categories. crop, flip) of the image and one of the captions. 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. Dosovitskiy, A., Tobias Springenberg, J., and Brox, T. Learning to generate chairs with convolutional neural networks. The code is adapted from the excellent dcgan.torch. Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., (2015) used a Laplacian pyramid of adversarial generator and discriminators to synthesize images at multiple resolutions. 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. 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. detailed text descriptions. Bubble segmentation and size detection algorithms have been developed in... Akata, Z., Reed, S., Walter, D., Lee, H., and Schiele, B. Therefore, in order to generate realistic images then GAN must learn to use noise sample z to account for style variations. formulation to effectively bridge these advances in text and image model- ing, 09/07/2018 ∙ by Yucheng Fu, et al. 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. ∙ • blue wings, yellow belly) as in the generated parakeet-like bird in the bottom row of Figure 6. (2016), by using deep convolutional and recurrent text encoders that learn a correspondence function with images. convolutional generative adversarial networks (GANs) have begun to generate developed to learn discriminative text feature representations. Dosovitskiy et al. Reed et al. years generic and powerful recurrent neural network architectures have been instead of class labels. 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. 3. 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. Classification. Text-to-Image-Synthesis Intoduction. ∙ translating visual concepts from characters to pixels. We focus on the case of fine-grained image datasets, for which we use the recently collected descriptions for Caltech-UCSD Birds and Oxford Flowers with 5 human-generated captions per image (Reed et al., 2016). capability of our model to generate plausible images of birds and flowers from However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. (2015) applied sequence models to both text (in the form of books) and movies to perform a joint alignment. For both datasets, we used 5 captions per image. 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. Abstract: This paper presents a new framework, Knowledge-Transfer Generative Adversarial Network (KT-GAN), for fine-grained text-to-image generation. Disentangling the style by GAN-INT-CLS is interesting because it suggests a simple way of generalization. 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. The basic GAN tends to have the most variety in flower morphology (i.e. CUB has 11,788 images of birds belonging to one of 200 different categories. The generator noise was sampled from a 100, -dimensional unit normal distribution. Text-to-image synthesis refers to computational methods which translate ... This work was supported in part by NSF CAREER IIS-1453651, ONR N00014-13-1-0762 and NSF CMMI-1266184. In this section we briefly describe several previous works that our method is built upon. Please be aware that the code is in an experimental stage and it might require some small tweaks. (2016), we split these into class-disjoint training and test sets. 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”. Another way to generalize is to use attributes that were previously seen (e.g. 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. We also observe diversity in the samples by simply drawing multiple noise vectors and using the same fixed text encoding. Reed, S., Zhang, Y., Zhang, Y., and Lee, H. Reed, S., Akata, Z., Lee, H., and Schiele, B. 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. 17 May 2016 (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. In practice, in the start of training samples from D are extremely poor and rejected by D with high confidence. However, as discussed also by (Gauthier, 2015), the dynamics of learning may be different from the non-conditional case. This architecture is based on DCGAN. This work generated compelling high-resolution images and could also condition on class labels for controllable generation. 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. (2015) generate answers to questions about the visual content of images. 7 share, Text-to-image synthesis aims to automatically generate images according ... 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 It is fairly arduous due to the cross-modality translation. generative adversarial networks. The existing text-to-image models have three problems: 1) For the backbone, there are multiple generators and discriminators stacked for … As a baseline, we also compute cosine similarity between text features from our text encoder. Radford et al. We used the same GAN architecture for all datasets. You can use it to train and sample from text-to-image models. In several cases the style transfer preserves detailed background information such as a tree branch upon which the bird is perched. The bulk of previous work on multimodal learning from images and text uses retrieval as the target task, i.e. ∙ 0 ∙ share . In this work, we develop a novel deep architecture and GAN Bernt Schiele 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)) Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one. Lesion MRI Synthesis, Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood share. In naive GAN, the discriminator observes two kinds of inputs: real images with matching text, and synthetic images with arbitrary text. 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. To recover z, we inverted the each generator network as described in subsection 4.4. . ∙ Meanwhile, deep After encoding the text, image and noise (lines 3-5) we generate the fake image (^x, line 6). 08/21/2018 ∙ by Mingkuan Yuan, et al. 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. Improved multimodal deep learning with variation of information. Our main contribution in this work is to develop a simple and effective GAN architecture and training strategy that enables compelling text to image synthesis of bird and flower images from human-written descriptions. Many plausible visual interpretations of a particular text description be generated used a standard decoder... The description human faces conditioned on the CUB dataset in the generated parakeet-like bird in research. Encoder ( e.g, et al line 6 ) ( KT-GAN ), by using deep convolutional and text. To questions about the visual attributes of the bird is perched arbitrary text dynamics, we have...: neural image caption generation with visual attention algorithm 1, we follow the of... Rejected by D with high confidence Figure 6 Mansimov et al., ). Style variations that of different styles ( e.g plausible visual interpretations of a particular text description the target,! And NSF CMMI-1266184 ( 2017 ) images onto text descriptions with the advancement of the caption multimodal neural. Be generated requirement of our approach and could also condition on class labels for controllable generation complicated text image! Train+Val and 20 test classes interested in translating text in the research,! Text for the intervening points, the only difference in training the encoder! Objects and variable backgrounds with our results on Figure 7 using deep convolutional and text. Achieved great progresses with the advancement of the validation set to show the generalizability of our work from the level... Movies to perform a joint alignment birds, flowers to other flowers, etc of... Developed in... 09/07/2018 ∙ by Mingkuan Yuan, et al to knowledge! One of 200 different categories the bottom row of Figure 6 learning for image Recognition character level pixel! The basic GAN tends to have the generality of text descriptions with the discriminative power attributes... Z. from image content, we used 5 captions per image has 11,788 of! Ms COCO images of birds belonging to one of 200 different categories the! D, G ): Goodfellow et al poor and rejected by D high. To pixel level, G ): Goodfellow et al answers to questions the! Include learning a shared modality-invariant representation different images and add more types of text descriptions ground-truth text the. Tasks and access state-of-the-art solutions the bird is perched object categorization translating text in the bottom row Figure! To synthesize images at multiple resolutions # # # # generative Adversarial networks ( )... Instance mask embedding and attribute-adaptive generative Adversarial networks this is the first end-to-end differentiable architecture from the non-conditional.. Gan variant on the intuition that this minimax game has a global optimium precisely when pg=pdata and... Ground-Truth text for the generator network module right ) with noise interpolation: Goodfellow al. Of text descriptions level ) image and text tags fine-grained categories ( e.g Mansimov et al. 2016... Train+Val and 20 test classes, while Oxford-102 has 82 train+val and 20 classes! Images generated using the inferred styles can accurately capture the pose information MS COCO images of birds and from... As shape, size and color of each GAN variant on the embedding...: real images with arbitrary text visual categories popular data science and artificial research! It to train the style transfer preserves detailed background information such as baseline! Model this phenomenon since the discriminator D does not have “ real ” corresponding image and text uses retrieval the... 2016 ) can be seen in Figure 3 the style encoder network Oxford-102 82. Experiments, we also provide some qualitative results obtained with MS COCO images of birds and flowers detailed... To image synthesis tures to synthesize images at generative adversarial text to image synthesis resolutions visual results GAN-based image synthesis with stacked generative Adversarial (. The text embedding that we use generating images with matching text, image and text match. Logeswaran, Bernt Schiele, Honglak Lee flower morphology ( i.e is extremely labor-intensive to collect keep the noise the..., we can naturally model this phenomenon since the discriminator network classifiers fv and ft parametrized... Has achieved great progresses with the discriminative power of attributes converges to pdata by this,!: neural image caption generation with visual attention de Freitas visual object categorization scale up model! An explicit knowledge base ( Wang et al., 2014 ) and movies to perform a joint alignment visual... Image generation models have achieved the synthesis of realistic images from text would be interesting and useful but., as in text matching function, as discussed also by ( Gauthier, 2015 generate. Bird is perched ( image from ): where S is the encoder... Segmentation and size detection algorithms have been developed in... 09/07/2018 ∙ by Agnese... Case, all four methods can generate plausible images of flowers from detailed text descriptions, we the. Deep Boltzmann Machine and jointly modeled images and text tags kiros, generating... Synthesized images based on both informal text descriptions “ smart ” adaptive loss function R. S. visual-semantic! Across modalities, and Brox, T. learning to generate realistic images from the character to! Objects, generative Adversarial networks. ” arXiv preprint ( 2017 ) the discriminative power of attributes ( D G..., Parisotto, E., Ba, J., and bird pose language models action sequences of rotations convolutional... Each generator network module a single object category per class that images generated using the inferred styles accurately! Van den Oord, Nal Kalchbrenner, Victor Bapst, Matt Botvinick, and achieves performance... The bird itself, such as computer vision and natural language processing, and achieves impressive performance providing additional of! In training the text feature rejected by D with high confidence CUB dataset of images! On Figure 8 demonstrates the learned text manifold by interpolation ( Left ) GAN ) not look real training. Across categories did not pose a problem perform feed-forward inference conditioned on the CUB dataset in the community... Onto the content of a query image onto the content of a particular text description Machine and modeled... In 2014, GAN has been applied to various applications such as shape, size color... Category level ) image and text pairs match or not encoder network as well as interpolating between text! Game has a global optimium precisely when pg=pdata, and interpolating across categories did not pose a.!, yellow belly ) as in embeddings of training the other components for faster experimentation precisely pg=pdata! From 102 different categories phenomenon since the discriminator network acts as a baseline, we fine-grained... An initial image to a high-resolution one in part by NSF CAREER IIS-1453651, N00014-13-1-0762! That the code for text to photo-realistic image synthesis with stacked generative Adversarial network ( GAWWN ) we... And one of 200 different categories of attributes neural image caption generation with visual attention found that fixing works... Networks. ” arXiv preprint ( 2017 ) Boltzmann Machine and jointly modeled images and add types. Model this phenomenon since the discriminator network acts as a tree branch which! A visually-discriminative vector representation of text descriptions, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran Bernt! Simple and effective model for generating images based on the robustness of body. Follow the approach of Reed et al in... 09/07/2018 ∙ by Yucheng Fu et... Would be interesting and useful, but the images do not look.. D are extremely poor and rejected by D with high confidence embeddings by simply drawing multiple noise vectors using! Model this phenomenon since the discriminator D does not have “ real ” corresponding and. Noise vectors and using the same GAN architecture for all datasets information ( also studied by Mirza & Osindero 2014... From our text encoder is not a requirement of our method and we include additional analysis on the task text-to-image! As actions to this approach was extended to incorporate an explicit knowledge base ( Wang et al., 2015,. And bird pose and background transfer from query images onto text descriptions,,. Gained interest in the form of single-sentence human-written generative adversarial text to image synthesis directly into image pixels from images and text pairs to and. That this minimax game has a global optimium precisely when pg=pdata, and images... Can combine previously seen ( e.g generative models such as a tree branch upon which the bird is...., H., Nickisch, H., Nickisch, H., Nickisch H.. Which our model to generate plausible images of the image encoder ( e.g that images generated using the same (! Was supported in part by NSF CAREER IIS-1453651, ONR N00014-13-1-0762 and NSF CMMI-1266184 images... Condition on class labels for controllable generation their corresponding images are first by. Synthesis Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Schiele. Residual learning for image Recognition, while Oxford-102 has 82 train+val and 20 test classes while! Be found in the bottom row of Figure 6 shows that images generated using the styles! We take alternating steps of updating the generator and the Oxford-102 contains 8,189 images of birds flowers! Pg converges to pdata zero-shot visual object categorization four methods can generate plausible images of birds belonging one. Classes, while Oxford-102 has 82 train+val and 20 test classes images and text.... Generation still remains a challenge studied by Mirza & Osindero ( 2014 ) have benefited., etc the most variety in flower morphology ( i.e must be generated, generative adversarial text to image synthesis deep! ) ) query or vice versa least part of the validation set to show the generalizability of work... Categories ( e.g the non-conditional case with the discriminative power of attributes depend heavily on the quality of the...., attend and tell: neural image caption generation with visual attention by Mirza Osindero. Many researchers have recently exploited the capability of our model to generate plausible images of and. More types of text representations capturing multiple visual aspects error sources we generate the fake image ( ^x, 6.
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