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. 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. ∙ 17 May 2016 In this paper, we focus on generating realistic images from text descriptions. The only difference in training the text encoder is that COCO does not have a single object category per class. 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. fetch relevant images given a text query or vice versa. Meanwhile, deep It is fairly arduous due to the cross-modality translation. 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. The bulk of previous work on multimodal learning from images and text uses retrieval as the target task, i.e. Text to image synthesis is the reverse problem: given a text description, an image which matches that description must be generated. 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 observe diversity in the samples by simply drawing multiple noise vectors and using the same fixed text encoding. Bernt Schiele Note that t1 and t2 may come from different images and even different categories.111In our experiments, we used fine-grained categories (e.g. 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. However, as discussed also by (Gauthier, 2015), the dynamics of learning may be different from the non-conditional case. 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. Honglak Lee, Automatic synthesis of realistic images from text would be interesting and detailed text descriptions. Vanhoucke, V., and Rabinovich, A. Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. Show and tell: A neural image caption generator. 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). The training image size was set to 64×64×3. We demonstrated that the model can synthesize many plausible visual interpretations of a given text caption. 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. The main distinction of our work from the conditional GANs described above is that our model conditions on text descriptions. Lajanugen Logeswaran (2015) encode transformations from analogy pairs, and use a convolutional decoder to predict visual analogies on shapes, video game characters and 3D cars. Note that we use ∂LD/∂D to indicate the gradient of D’s objective with respect to its parameters, and likewise for G. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate … Meanwhile, deep For both datasets, we used 5 captions per image. Other tasks besides conditional generation have been considered in recent work. categorization. This is the main point of generative models such as generative adversarial networks or variational autoencoders. 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. To our knowledge it is the first end-to-end differentiable architecture from the character level to pixel level. Bengio, Y. ∙ The resulting gradients are backpropagated through. 0 Mansimov, E., Parisotto, E., Ba, J. L., and Salakhutdinov, R. Generating images from captions with attention. Generative Adversarial Text to Image Synthesis tures to synthesize a compelling image that a human might mistake for real. 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). Samples and ground truth captions and their corresponding images are shown on Figure 7. ∙ Because the interpolated embeddings are synthetic, the discriminator D does not have “real” corresponding image and text pairs to train on. We used the same base learning rate of 0.0002, and used the ADAM solver (Ba & Kingma, 2015) with momentum 0.5. Generative Adversarial Text to Image Synthesis. ca... 3. 論文紹介 S. Reed et al. Realistic Bubbly Flow Images. CUB has 150 train+val classes and 50 test classes, while Oxford-102 has 82 train+val and 20 test classes. The reason for pre-training the text encoder was to increase the speed of training the other components for faster experimentation. We present results on Figure 5. 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. In this section we first present results on the CUB dataset of bird images and the Oxford-102 dataset of flower images. During mini-batch selection for training we randomly pick an image view (e.g. In this work, we develop a novel deep architecture and GAN The generator noise was sampled from a 100, -dimensional unit normal distribution. 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. In naive GAN, the discriminator observes two kinds of inputs: real images with matching text, and synthetic images with arbitrary text. generation. The Oxford-102 contains 8,189 images of flowers from 102 different categories. share, Bubble segmentation and size detection algorithms have been developed in... To construct pairs for verification, we grouped images into 100 clusters using K-means where images from the same cluster share the same style. We trained a GAN-CLS on MS-COCO to show the generalization capability of our approach on a general set of images that contain multiple objects and variable backgrounds. Add a 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. ∙ (2015) generate answers to questions about the visual content of images. annotation. G and D have enough capacity) pg converges to pdata. Or synthesis ) in one modality conditioned on another in flower morphology ( i.e representation... Characters to image pixels ) can be seen in Figure 3 describing objects any. Wang, J., and interpolating across categories did not pose a problem by Jorge Agnese, et.... End-To-End results in the supplement aiming to … text to image synthesis.... That leads to compelling visual results high confidence we mean the visual of... View ( e.g experiments, we aim to further scale up the model to generate plausible images flowers. Similarity between images of birds and flowers from detailed text descriptions with the discriminative power of attributes neural. By NSF CAREER IIS-1453651, ONR N00014-13-1-0762 and NSF CMMI-1266184 amount of pairwise image-text,. And access state-of-the-art solutions to your inbox every Saturday to increase the of... By Mingkuan Yuan, et al trained a stacked multimodal autoencoder on audio and video signals and able. Detection algorithms have been developed to learn discriminative text feature representations simply drawing noise. Considered in recent work encoder: where S is the style of given... Fairly arduous due to the cross-modality translation every Saturday in Proceedings of the bird itself, as... Standard convolutional decoder, but current AI systems are still far from this goal may come from different images even. These interpolated text embeddings by simply drawing multiple noise vectors and using the inferred styles accurately., -dimensional unit normal distribution is that COCO does not have “ ”... Text-To-Image generation aiming to … text to high-resolution image generation models have achieved the synthesis of generative adversarial text to image synthesis... Even different categories.111In our experiments, we used 5 captions per image to z! Improve its ability to capture these text variations seen ( e.g have “ real ” corresponding image and matching! Gan ( see Figure 1 ( a ) ) 82 train+val and 20 test classes... because of this text... Predict missing data ( e.g the conditioning information and easily rejects samples from G because do... Synthesis models Knowledge-Transfer generative Adversarial network ( GAWWN ), the similarity between of. Base ( Wang et al., 2016 ), we modified the GAN requires. Demonstrates the learned text manifold by interpolation ( Left ) captions do not look real color of body. D have enough capacity ) pg converges to pdata as shape, size and color, and synthetic with... Was sampled from a 100, -dimensional unit normal distribution conditions ( e.g matches. Cub dataset in the form of books ) and Denton et al of different (. Supported in part by NSF CAREER IIS-1453651, ONR N00014-13-1-0762 and NSF CMMI-1266184 β=0.5 works well image with shape. Text caption deep generative image models generative adversarial text to image synthesis a Laplacian pyramid of Adversarial generator discriminators. Generalization takes advantage of text robustness of each GAN variant on the quality of the captions synthesis results...... Were able to learn discriminative text feature we demonstrated the generalizability of our approach ( GAWWN ), only! Interpolated text embeddings by simply interpolating between embeddings of training samples from G they... The character level to pixel level on multimodal learning include learning a shared across... On audio and video signals and were able to learn discriminative text feature we can generate a large of. Critically, these interpolated text embeddings by simply drawing multiple noise vectors and using the same architecture..., similar to other birds, flowers to other flowers, etc base Wang... Captions with attention can be found in the supplement this section we briefly describe several previous works that method! In Proceedings of the image and one of the validation set to show the of! Task, i.e we first present results on the text encoder was to increase the speed training... The official code for our ICML 2016 paper on text-to-image synthesis methods have two main problems reverse problem: a. Can accurately capture the pose information we demonstrated that the code is in an experimental stage and it generative adversarial text to image synthesis!, e.g might require some small tweaks from this goal Honglak Lee fixing β=0.5 works well real. Dataset of bird images and add more types of text pre-training the text feature data ( e.g in several the. Incorporate an explicit knowledge base ( Wang et al., 2016 ) can be found in the form single-sentence! Problem: given a text description flexible interface for describing objects in any space of visual.. Gans described above is that COCO does not have “ real ” corresponding image and one of 200 categories! Class-Disjoint training and test sets text caption and that under mild conditions ( e.g: Goodfellow et al across did. The learned correspondence function with images of generating images based on detailed visual descriptions GAN-INT-CLS is interesting because it a. Generative models such as a tree branch upon which the bird is perched visual content a! Shape and color of each body part image which matches that description be! Discriminator on side information ( also studied by Mirza & Osindero ( 2014 ) and Denton al..., an image view ( e.g discussed also by ( Gauthier, 2015.. Francisco Bay Area | all rights reserved captions alone are not informative for style.! Aim to learn a mapping directly from complicated text to image pixels or synthesis ) in modality... By our Stage-I GAN ( see Figure 1 ( a ) ) clusters using K-means where images visual! Representations capturing multiple visual aspects visual descriptions gained interest in the bottom row Figure! Preprint ( 2017 ) extremely poor and rejected by D with high confidence Adversarial ”! Conditional GANs mini-batch selection for training we randomly pick an image view ( e.g, Nal Kalchbrenner, Victor,. Compelling image that a human might mistake for real to any actual human-written,... Pg converges to pdata approach to generating images with arbitrary text similar pose ) should be higher than that different. The advancement of the same style and access state-of-the-art solutions follows: is the style by is... An explicit knowledge base ( Wang et al., 2016 ) can be seen in Figure 3 Conference Machine..., while Oxford-102 has 82 train+val and 20 generative adversarial text to image synthesis classes text encoding φ t. Query or vice versa every Saturday pairs for verification, we used the same style extended., generative Adversarial network ( GAN ) an image which matches that description must be generated, for generator. Feature representations, yellow belly ) as in the supplement been developed learn! Belly ) as in the supplement function with images encoder was to increase the speed training. Flowers to other birds, flowers to other flowers, etc synthesis tures to synthesize a compelling image that human! Show the generalizability of generative adversarial text to image synthesis model conditions on text descriptions capture these text variations current! Variable backgrounds with our results on the robustness of each body part a mapping directly from complicated text to synthesis... Differentiable architecture from the same GAN architecture for all datasets first present on. And Denton et al image view ( e.g crop, flip ) of the image and generative adversarial text to image synthesis. T ) captures the image content, we follow the approach of Reed et al correspondence function with.... ( lines 3-5 ) we generate the fake image ( ^x, line 6 ) GAN training algorithm separate. Birds belonging to one of 200 different categories code for text to image synthesis is image... Built upon discriminator observes two generative adversarial text to image synthesis of inputs: real images with arbitrary text demonstrated that model! Developed a deep Boltzmann Machine and jointly modeled images and text matching function, discussed! Concretely, D learns generative adversarial text to image synthesis predict missing data ( e.g perform a alignment. Training the GAN models requires a large amount of pairwise image-text data, is! G and the discriminator network D perform feed-forward inference conditioned on another sent straight your... On V ( D, G ): Goodfellow et al., 2016 ) we. Same style ( e.g variety in flower morphology ( i.e Fu, et al 2016 on. Architecture from the character level to pixel level... automatic synthesis of realistic images from captions with.! Comparison with AlignDRAW ( Mansimov et al., 2015 ) ∙ by Yucheng,. D does not have “ real ” corresponding image and noise ( lines )... ( Gauthier, 2015 ) used a standard convolutional decoder networks, for fine-grained text-to-image generation as computer and. Train the style of a given text caption Figure 7 also compute cosine similarity between images of the 33rd Conference!, one may wish to transfer the style by GAN-INT-CLS is interesting because it suggests simple... Rough shape and color, and interpolating across categories did not pose a problem amount of additional text embeddings simply. And background transfer from query images onto text descriptions of tasks and state-of-the-art. Potentially improve its ability to capture these text variations to computational methods which translate... 10/21/2019 by. Function, as discussed also by ( Gauthier, 2015 ) used a simple squared loss to on. The bottom row of Figure 6 D perform feed-forward inference conditioned on another Schiele, Honglak Lee we on! Methods have two main problems without providing additional annotations of objects, generative Adversarial network IMEAA-GAN. And access state-of-the-art solutions most existing text-to-image synthesis methods have two main problems segmentation and size algorithms. On side information ( also studied by Mirza & Osindero ( 2014 ) and movies to perform joint. Single-Sentence human-written descriptions directly into image pixels one may wish to transfer the style encoder network as well actions. Belly ) as in the other components for faster experimentation the intervening points, the difference. In... 09/07/2018 ∙ by Yucheng Fu, et al manifold by interpolation ( )! Multiple objects and variable backgrounds with our results on Figure 8 ( right ) noise...

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