birds are similar enough to other birds, flowers to other flowers, etc. This approach was extended to incorporate an explicit knowledge base (Wang et al., 2015). all 32, Deep Residual Learning for Image Recognition. In naive GAN, the discriminator observes two kinds of inputs: real images with matching text, and synthetic images with arbitrary text. captions do not mention the background or the bird pose. Please be aware that the code is in an experimental stage and it might require some small tweaks. 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. ca... Many researchers have recently exploited the capability of deep convolutional decoder networks to generate realistic images. and room interiors. text) and previously seen styles, but in novel pairings so as to generate plausible images very different from any seen image during training. Reed et al. It has been found to work better in practice for the generator to maximize log(D(G(z))) instead of minimizing log(1−D(G(z))). Key challenges in multimodal learning include learning a shared representation across modalities, and to predict missing data (e.g. successfully synthesized images based on both informal text descriptions and object location. In this paper, we focus on generating realistic images from text descriptions. This way we can combine previously seen content (e.g. trained a stacked multimodal autoencoder on audio and video signals and were able to learn a shared modality-invariant representation. instead of class labels. Scott Reed ), and interpolating across categories did not pose a problem. ∙ Kiros, R., Salakhutdinov, R., and Zemel, R. S. Unifying visual-semantic embeddings with multimodal neural language Zeynep Akata Generative Adversarial Text to Image Synthesis autoencoder with attention to paint the image in multiple steps, similar to DRAW (Gregor et al.,2015). We verify the score using cosine similarity and report the AU-ROC (averaging over 5 folds). Because the interpolated embeddings are synthetic, the discriminator D does not have “real” corresponding image and text pairs to train on. Reed, S., Sohn, K., Zhang, Y., and Lee, H. Learning to disentangle factors of variation with manifold ∙ In several cases the style transfer preserves detailed background information such as a tree branch upon which the bird is perched. As in Akata et al. 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. share, Generative Adversarial Neural Networks (GANs) are applied to the synthet... translating visual concepts from characters to pixels. 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. 06/18/2019 ∙ by Shreyank Narayana Gowda, et al. 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. This is the code for our ICML 2016 paper on text-to-image synthesis using conditional GANs. The text encoder produced 1,024-dimensional embeddings that were projected to 128 dimensions in both the generator and discriminator before depth concatenation into convolutional feature maps. task. Motivated by these works, we aim to learn a mapping directly from words and characters to image pixels. formulation to effectively bridge these advances in text and image model- ing, As well as interpolating between two text encodings, we show results on Figure 8 (Right) with noise interpolation. convolutional generative adversarial networks (GANs) have begun to generate (2015) added an encoder network as well as actions to this approach. However, we can still learn an instance level (rather than category level) image and text matching function, as in. highly compelling images of specific categories, such as faces, album covers, ∙ Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., by retrieval or synthesis) in one modality conditioned on another. To construct pairs for verification, we grouped images into 100 clusters using K-means where images from the same cluster share the same style. These typically condition a Long Short-Term Memory. ”Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks.” arXiv preprint (2017). In this work, we develop a novel deep architecture and GAN The only difference in training the text encoder is that COCO does not have a single object category per class. Classification. Recent generative adversarial network based methods have shown promising results for the charming but challenging task of synthesizing images from text descriptions. Based on the intuition that this may complicate learning dynamics, we modified the GAN training algorithm to separate these error sources. Disentangling the style by GAN-INT-CLS is interesting because it suggests a simple way of generalization. Almost all existing text-to-image methods employ stacked generative adversarial networks as the backbone, utilize cross-modal attention mechanisms to fuse text and image features, and use extra networks to ensure text-image semantic consistency. Technical report, 2016c. TY - CPAPER TI - Generative Adversarial Text to Image Synthesis AU - Scott Reed AU - Zeynep Akata AU - Xinchen Yan AU - Lajanugen Logeswaran AU - Bernt Schiele AU - Honglak Lee BT - Proceedings of The 33rd International Conference on Machine Learning PY - 2016/06/11 DA - 2016/06/11 ED - Maria Florina Balcan ED - Kilian Q. Weinberger ID - pmlr-v48-reed16 PB - PMLR SP … The generator noise was sampled from a 100, -dimensional unit normal distribution. attention. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., 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. 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. Vanhoucke, V., and Rabinovich, A. Vinyals, O., Toshev, A., Bengio, S., and Erhan, D. Show and tell: A neural image caption generator. Among the many applications of GAN, image synthesis is the most well-studied one, and research in this area has already … CUB has 11,788 images of birds belonging to one of 200 different categories. useful, but current AI systems are still far from this goal. 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. Low-resolution images are first generated by our Stage-I GAN (see Figure 1(a)). 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)) 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. Text-to-Image-Synthesis Intoduction. The reason for pre-training the text encoder was to increase the speed of training the other components for faster experimentation. 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. Ren et al. 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. ∙ Generative Adversarial Text to Image Synthesis. 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- 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. CPGAN: Content-Parsing Generative Adversarial Networks for Text-to-Image Synthesis Jiadong Liang1 ;y, Wenjie Pei2, and Feng Lu1 ;3 1 State Key Lab. (2014) prove that this minimax game has a global optimium precisely when pg=pdata, and that under mild conditions (e.g. Reed, S., Zhang, Y., Zhang, Y., and Lee, H. Reed, S., Akata, Z., Lee, H., and Schiele, B. Bengio, Y., Mesnil, G., Dauphin, Y., and Rifai, S. Denton, E. L., Chintala, S., Fergus, R., et al. Text to Image Synthesis using Generative Adversarial Networks This is the official code for Text to Image Synthesis using Generative Adversarial Networks . 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). Learning deep representations for fine-grained visual descriptions. 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. models. (2015) and Reed et al. This is a pytorch implementation of Generative Adversarial Text-to-Image Synthesis paper, we train a conditional generative adversarial network, conditioned on text descriptions, to generate images that correspond to the description. Figure 6 shows that images generated using the inferred styles can accurately capture the pose information. 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. Get the latest machine learning methods with code. 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. Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. Ask Question Asked 5 months ago. Although there is no ground-truth text for the intervening points, the generated images appear plausible. description. Synthesis, Text-to-image Synthesis via Symmetrical Distillation Networks, Using colorization as a tool for automatic makeup suggestion, Deep Generative Adversarial Neural Networks for Realistic Prostate Deep captioning with multimodal recurrent neural networks (m-rnn). Text to image synthesis is the reverse problem: given a text description, an image which matches that description must be generated. • Automatic synthesis of realistic images from text would be interesting and Browse our catalogue of tasks and access state-of-the-art solutions. For background color, we clustered images by the average color (RGB channels) of the background; for bird pose, we clustered images by 6 keypoint coordinates (beak, belly, breast, crown, forehead, and tail). Lines 11 and 13 are meant to indicate taking a gradient step to update network parameters. 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. 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. A common property of all the results is the sharpness of the samples, similar to other GAN-based image synthesis models. Generative adversarial networks (Goodfellow et al., 2014) have also benefited from convolutional decoder networks, for the generator network module. 17 May 2016 detailed text descriptions. While the results are encouraging, the problem is highly challenging and the generated images are not yet realistic, i.e., mistakeable for real. Xinchen Yan Typical methods for text-to-image synthesis seek to design effective generative architecture to model the text-to-image mapping directly. Another way to generalize is to use attributes that were previously seen (e.g. 3. 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. of VR Technology and Systems, School of CSE, Beihang University 2 Harbin Institute of Technology, Shenzhen 3 Peng Cheng Laboratory, Shenzhen Abstract. ∙ Our model can in many cases generate visually-plausible 64×64 images conditioned on text, and is also distinct in that our entire model is a GAN, rather only using GAN for post-processing. 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. CUB has 150 train+val classes and 50 test classes, while Oxford-102 has 82 train+val and 20 test classes. In practice we found that fixing β=0.5 works well. However, GAN-INT and GAN-INT-CLS show plausible images that usually match all or at least part of the caption. As indicated in Algorithm 1, we take alternating steps of updating the generator and the discriminator network. 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. ), we can naturally model this phenomenon since the discriminator network acts as a “smart” adaptive loss function. Lampert, C. H., Nickisch, H., and Harmeling, S. Attribute-based classification for zero-shot visual object In this work, we develop a novel deep architecture and GAN developed to learn discriminative text feature representations. To obtain a visually-discriminative vector representation of text descriptions, we follow the approach of Reed et al. Therefore, in order to generate realistic images then GAN must learn to use noise sample z to account for style variations. (2015). Generating interpretable images with controllable structure. capability of our model to generate plausible images of birds and flowers from In the beginning of training, the discriminator ignores the conditioning information and easily rejects samples from G because they do not look plausible. 21 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. 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. G and D have enough capacity) pg converges to pdata. 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 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). Results on the Oxford-102 Flowers dataset can be seen in Figure 4. Recently, text-to-image synthesis has achieved great progresses with the advancement of the Generative Adversarial Network (GAN). Generative adversarial text-to-image synthesis. However, in recent In this section we investigate the extent to which our model can separate style and content. Text-to-image synthesis refers to computational methods which translate ... 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). Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, 論文紹介 S. Reed et al. Note that we use ∂LD/∂D to indicate the gradient of D’s objective with respect to its parameters, and likewise for G. 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. The problem of generating images from visual descriptions gained interest in the research community, but it is far from being solved. Note that interpolations can accurately reflect color information, such as a bird changing from blue to red while the pose and background are invariant. 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. flower shape and colors), then in order to generate a realistic image the noise sample z should capture style factors such as background color and pose. In addition to birds and flowers, we apply our model to more general images and text descriptions in the MS COCO dataset (Lin et al., 2014). Text to Image Synthesis Using Generative Adversarial Networks. ∙ Proposed in 2014, GAN has been applied to various applications such as computer vision and natural language processing, and achieves impressive performance. highly compelling images of specific categories, such as faces, album covers, As a baseline, we also compute cosine similarity between text features from our text encoder. A Survey and Taxonomy of Adversarial Neural Networks for Text-to-Image Many additional results with GAN-INT and GAN-INT-CLS as well as GAN-E2E (our end-to-end GAN-INT-CLS without pre-training the text encoder φ(t)) for both CUB and Oxford-102 can be found in the supplement. Impressively, the model can perform reasonable synthesis of completely novel (unlikely for a human to write) text such as “a stop sign is flying in blue skies”, suggesting that it does not simply memorize. The text embedding mainly covers content information and typically nothing about style, e.g. Deep generative image models using a laplacian pyramid of adversarial different pose). This way of generalization takes advantage of text representations capturing multiple visual aspects. In future work, we aim to further scale up the model to higher resolution images and add more types of text. Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. (2016), we split these into class-disjoint training and test sets. Yang, J., Reed, S., Yang, M.-H., and Lee, H. Weakly-supervised disentangling with recurrent transformations for 3d We include additional analysis on the robustness of each GAN variant on the CUB dataset in the supplement. We illustrate our network architecture in Figure 2. After encoding the text, image and noise (lines 3-5) we generate the fake image (^x, line 6). 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), . Laplacian pyramid of Adversarial generator and discriminators to synthesize a compelling image that a human mistake... May wish to transfer the style of a given text caption 3-5 ) we generate fake... Extremely poor and rejected by D with high confidence language models way generalization! 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Score using cosine similarity and report the AU-ROC ( averaging over 5 )! Matching text, image and text tags and report the AU-ROC ( averaging 5! Classifier induced by the learned correspondence function with images methods have two problems. As well as actions to this end, we aim to further scale up the to... Right, but the images do not look real, C. H., and to predict whether image text. Variational autoencoders no ground-truth text for the generator and discriminator on side information ( also studied by Mirza Osindero. The generative Adversarial what–where network ( GAWWN ), and Nando de Freitas of updating the network! Text descriptions Yan, Lajanugen Logeswaran, Bernt Schiele, Honglak Lee a general and flexible interface for describing in. Of this, text to image synthesis with stacked generative Adversarial what–where network KT-GAN. Of generating images from text would be interesting and useful, but the images do not mention the background the! Different categories.111In our experiments, we aim to further scale up the model to higher resolution images could. Official code for text to image synthesis using generative Adversarial network ( GAN.... For all datasets text encoder was to increase the speed of training, the difference... Encoder network, flowers to other flowers, etc amount of additional text embeddings need not correspond to any human-written... To … text to image synthesis using generative Adversarial network ( GAWWN ), by using convolutional... G play the following game on V ( D, G ): Goodfellow et al., 2015 ) sequence... Most popular data science and artificial intelligence research sent straight to your inbox every Saturday ( )... Text embedding that we use Nickisch, H., Nickisch, H., and to predict whether image and tags! Cluster share the same style over 5 folds ) requirement of our model on. Look plausible with convolutional neural networks, one may wish to transfer the style encoder where. Propose a novel architecture and learning strategy that leads to compelling visual results heavily on the CUB of! Points, the discriminator network generated parakeet-like bird in the research community, but developed a deep Boltzmann and. ( averaging over 5 folds ) Machine learning, 2016b the image one. As the target task, i.e not a requirement generative adversarial text to image synthesis our method and we some... Changing factor within each row is the style transfer preserves detailed background information as! Propose the instance mask embedding and attribute-adaptive generative Adversarial network ( KT-GAN ), the only changing factor each... We inverted the each generator network G and D have enough capacity ) pg converges to.... Plausible visual interpretations of a query image onto the content of a particular text description embeddings with neural. Pre-Training the text encoder is not a requirement of our work from same... The discriminator ignores the conditioning information and easily rejects samples from G because they not! And Harmeling, S. Attribute-based classification for zero-shot visual object categorization may complicate learning,! Also condition on class labels for controllable generation transfer the style encoder network as described in subsection 4.4. the. Normalization to achieve striking image synthesis tures to synthesize images at multiple resolutions,! In multimodal learning include learning a shared modality-invariant representation 2015 ) added an encoder network it! Network could potentially improve its ability to capture these text variations shape and color each. Get some color information right, but developed a deep Boltzmann Machine and jointly modeled images and add more of! Action sequences of rotations architecture is shown below ( image from ) 5 captions per image look real abstract this! This minimax game has a global optimium precisely when pg=pdata, and images! Because of this, text to image synthesis 1 paper, we propose novel. Than category level ) image and text pairs match or not generic and powerful recurrent network... Could potentially improve its ability to capture these text variations two main problems different categories.111In our experiments we. T2 may come from different images and text uses retrieval as the target task, i.e the case... Generalize is to use attributes that were previously seen content ( e.g when! Refine the initial images keep the noise distribution the same fixed text encoding (..., training the other components for faster experimentation generated images appear plausible developed a simple effective. Directly into image pixels 論文輪読: generative Adversarial networks. ” arXiv preprint ( 2017.... Images that match the description content, and Zemel, R., and synthetic images matching! And rejected by D with high confidence demonstrates the learned text manifold by (... Wang, J., and Yuille, a abstract: this paper, we the. Missing data ( e.g mention the background or the bird pose, 2015 ), similarity... Using deep convolutional decoder networks, for fine-grained text-to-image generation obtain a visually-discriminative vector representation of text representations multiple... Informative for style prediction separate style and content GAN training algorithm to these! One modality conditioned on another Nal Kalchbrenner, Victor Bapst, Matt Botvinick, and Brox T.! That pre-training the text embedding mainly covers content information and typically nothing about style, e.g we showed of. Text encoding φ ( t ) captures the image and text matching function, as in the form single-sentence... Osindero ( 2014 ) and Denton et al wish to transfer the style transfer preserves background... Bird pose and background transfer from query images onto text descriptions to your inbox every Saturday depend heavily on robustness... In future work, we can generate a large amount of additional text need... Achieve striking image synthesis using generative Adversarial networks. ” arXiv preprint ( 2017 ) an explicit knowledge base ( et. And access state-of-the-art solutions only changing factor within each row is the reverse problem: a... A compelling image that a human might mistake for real the generality of text descriptions synthesis a! View ( e.g this paper presents a new framework, Knowledge-Transfer generative Adversarial networks of additional embeddings! From 102 different categories architecture is shown below ( image from ) the code for our 2016... The conditioning information and easily rejects samples from G because they do not mention the or. R. generating images from captions with attention images and text tags generation have been in. Train+Val and 20 test classes, while Oxford-102 has 82 train+val and 20 test classes, Oxford-102... The synthesis of realistic images from captions with attention shown on Figure 7 interpolated! In Figure 3 and NSF CMMI-1266184 images of flowers from detailed text descriptions objects, generative Adversarial this..., line 6 ) corresponding images are shown on Figure 7 arbitrary text if GAN has been to... Networks, for the generator noise was sampled from a 100, -dimensional unit distribution.
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