Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., We also observe diversity in the samples by simply drawing multiple noise vectors and using the same fixed text encoding. With a trained GAN, one may wish to transfer the style of a query image onto the content of a particular text description. During mini-batch selection for training we randomly pick an image view (e.g. Typical methods for text-to-image synthesis seek to design 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. share, Many tasks in computer vision and graphics fall within the framework of 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. By content, we mean the visual attributes of the bird itself, such as shape, size and color of each body part. Deep generative image models using a laplacian pyramid of adversarial • We propose a novel architecture and learning strategy that leads to compelling visual results. A common property of all the results is the sharpness of the samples, similar to other GAN-based image synthesis models. 3. As a baseline, we also compute cosine similarity between text features from our text encoder. (2015), for details). A qualitative comparison with AlignDRAW (Mansimov et al., 2016) can be found in the supplement. Estimation, BubGAN: Bubble Generative Adversarial Networks for Synthesizing While the results are encouraging, the problem is highly challenging and the generated images are not yet realistic, i.e., mistakeable for real. The resulting gradients are backpropagated through. 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))). In the beginning of training, the discriminator ignores the conditioning information and easily rejects samples from G because they do not look plausible. This work generated compelling high-resolution images and could also condition on class labels for controllable generation. ca... Here, we sample two random noise vectors. However, training the GAN models requires a large amount of pairwise image-text data, which is extremely labor-intensive to collect. 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. Kiros, R., Salakhutdinov, R., and Zemel, R. S. Unifying visual-semantic embeddings with multimodal neural language 6 Add a If GAN has disentangled style using z. from image content, the similarity between images of the same style (e.g. 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. formulation to effectively bridge these advances in text and image model- ing, Learning deep representations for fine-grained visual descriptions. 0 CUB has 11,788 images of birds belonging to one of 200 different categories. In comparison, natural language offers a general and flexible interface for describing objects in any space of visual categories. ∙ Once G has learned to generate plausible images, it must also learn to align them with the conditioning information, and likewise D must learn to evaluate whether samples from G meet this conditioning constraint. Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., These approaches exceed the previous state-of-the-art using attributes for zero-shot visual recognition on the Caltech-UCSD birds database (Wah et al., 2011), and also are capable of zero-shot caption-based retrieval. view synthesis. Nilsback, Maria-Elena, and Andrew Zisserman. Dosovitskiy et al. 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. 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). 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. From a distance the results are encouraging, but upon close inspection it is clear that the generated scenes are not usually coherent; for example the human-like blobs in the baseball scenes lack clearly articulated parts. Classification. Lajanugen Logeswaran translating visual concepts from characters to pixels. Generative Adversarial Text to Image Synthesis autoencoder with attention to paint the image in multiple steps, similar to DRAW (Gregor et al.,2015). used a standard convolutional decoder, but developed a highly effective and stable architecture incorporating batch normalization to achieve striking image synthesis results. 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). models. ∙ share. 7 Critically, these interpolated text embeddings need not correspond to any actual human-written text, so there is no additional labeling cost. (2015). (2016) generated images from text captions, using a variational recurrent autoencoder with attention to paint the image in multiple steps, similar to DRAW (Gregor et al., 2015). 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. However, in recent 10/08/2016 ∙ by Scott Reed, et al. (2016), we split these into class-disjoint training and test sets. Reed et al. 論文輪読: Generative Adversarial Text to Image Synthesis 1. Finally we demonstrated the generalizability of our approach to generating images with multiple objects and variable backgrounds with our results on MS-COCO dataset. 0 ∙ 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. 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. To construct pairs for verification, we grouped images into 100 clusters using K-means where images from the same cluster share the same style. 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. Adam: A method for stochastic optimization. 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), . In several cases the style transfer preserves detailed background information such as a tree branch upon which the bird is perched. detailed text descriptions. Generating interpretable images with controllable structure. ∙ 0 ∙ share . Because the interpolated embeddings are synthetic, the discriminator D does not have “real” corresponding image and text pairs to train on. Low-resolution images are ﬁrst generated by our Stage-I GAN (see Figure 1(a)). The bulk of previous work on multimodal learning from images and text uses retrieval as the target task, i.e. Meanwhile, deep • Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday. convolutional generative adversarial networks (GANs) have begun to generate 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. Explicit knowledge-based reasoning for visual question answering. description. In this section we investigate the extent to which our model can separate style and content. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, 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. 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. The problem of generating images from visual descriptions gained interest in the research community, but it is far from being solved. In practice we found that fixing β=0.5 works well. (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. (2015) used a Laplacian pyramid of adversarial generator and discriminators to synthesize images at multiple resolutions. (2015) and Reed et al. 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. We used the same base learning rate of 0.0002, and used the ADAM solver (Ba & Kingma, 2015) with momentum 0.5. Results on the Oxford-102 Flowers dataset can be seen in Figure 4. In this section we briefly describe several previous works that our method is built upon. We include additional analysis on the robustness of each GAN variant on the CUB dataset in the supplement. Most existing text-to-image synthesis methods have two main problems. ∙ Proposed in 2014, GAN has been applied to various applications such as computer vision and natural language processing, and achieves impressive performance. 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. 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. Figure 8 demonstrates the learned text manifold by interpolation (Left). In future work, we aim to further scale up the model to higher resolution images and add more types of text. Generative Adversarial Text to Image Synthesis. In this work, we develop a novel deep architecture and GAN internal covariate shift. We demonstrate the watching movies and reading books. Text to Image Synthesis using Generative Adversarial Networks This is the official code for Text to Image Synthesis using Generative Adversarial Networks . For text features, we first pre-train a deep convolutional-recurrent text encoder on structured joint embedding of text captions with 1,024-dimensional GoogLeNet image embedings (Szegedy et al., 2015) as described in subsection 3.2. The main distinction of our work from the conditional GANs described above is that our model conditions on text descriptions. text) and previously seen styles, but in novel pairings so as to generate plausible images very different from any seen image during training. We demonstrated that the model can synthesize many plausible visual interpretations of a given text caption. Bubble segmentation and size detection algorithms have been developed in... Akata, Z., Reed, S., Walter, D., Lee, H., and Schiele, B. This way of generalization takes advantage of text representations capturing multiple visual aspects. Motivated by this property, we can generate a large amount of additional text embeddings by simply interpolating between embeddings of training set captions. However, we can still learn an instance level (rather than category level) image and text matching function, as in. 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). As well as interpolating between two text encodings, we show results on Figure 8 (Right) with noise interpolation. highly compelling images of specific categories, such as faces, album covers, Generative adversarial networks (Goodfellow et al., 2014) have also benefited from convolutional decoder networks, for the generator network module. Denton et al. 17 May 2016 Dosovitskiy, A., Tobias Springenberg, J., and Brox, T. Learning to generate chairs with convolutional neural networks. useful, but current AI systems are still far from this goal. This is the code for our ICML 2016 paper on text-to-image synthesis using conditional GANs. Saenko, K., and Darrell, T. Long-term recurrent convolutional networks for visual recognition and ... Reed, S., Sohn, K., Zhang, Y., and Lee, H. Learning to disentangle factors of variation with manifold For both datasets, we used 5 captions per image. 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. 7 ∙ Bengio, Y. DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis (A novel and effective one-stage Text-to-Image Backbone) Official Pytorch implementation for our paper DF-GAN: Deep Fusion Generative Adversarial Networks for Text-to-Image Synthesis by Ming Tao, Hao Tang, Songsong Wu, Nicu Sebe, Fei Wu, Xiao-Yuan Jing. ∙ captions do not mention the background or the bird pose. 0 In naive GAN, the discriminator observes two kinds of inputs: real images with matching text, and synthetic images with arbitrary text. The generator noise was sampled from a 100, -dimensional unit normal distribution. of VR Technology and Systems, School of CSE, Beihang University 2 Harbin Institute of Technology, Shenzhen 3 Peng Cheng Laboratory, Shenzhen Abstract. ∙ and room interiors. However, as discussed also by (Gauthier, 2015), the dynamics of learning may be different from the non-conditional case. 1.2 Generative Adversarial … 論文紹介 S. Reed et al. 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. AI image synthesis has made impressive progress since Generative Adversarial Networks (GANs) were introduced in 2014.GANs were originally only capable of generating small, blurry, black-and-white pictures, but now we can generate high-resolution, realistic and colorful pictures that you can hardly distinguish from real photographs. ∙ the problem of text to photo-realistic image synthesis into two more tractable sub-problems with Stacked Generative Adversarial Networks (StackGAN). by retrieval or synthesis) in one modality conditioned on another. 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. detailed text descriptions. Automatic synthesis of realistic images from text would be interesting and This work was supported in part by NSF CAREER IIS-1453651, ONR N00014-13-1-0762 and NSF CMMI-1266184. Person image synthesis Siamese generative adversarial network. 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. Reed, S., Zhang, Y., Zhang, Y., and Lee, H. Reed, S., Akata, Z., Lee, H., and Schiele, B. Multimodal learning with deep boltzmann machines. ∙ Zeynep Akata As in Akata et al. 08/01/2017 ∙ by Andy Kitchen, et al. We verify the score using cosine similarity and report the AU-ROC (averaging over 5 folds). Join one of the world's largest A.I. Mansimov, E., Parisotto, E., Ba, J. L., and Salakhutdinov, R. Generating images from captions with attention. 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Of reasonable individuals and complex but low-resolution images are ﬁrst generated by our Stage-I GAN ( see Figure 1 a... And could also condition on class labels for controllable generation steps of updating the network... Image content ( e.g actual human-written text, image and text tags encoder. Considered in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative feature... Gan must learn to use attributes that were previously seen ( e.g, line ). Style and content for describing objects in any space of visual categories text variations a Laplacian pyramid Adversarial. Noise vectors and using the inferred styles can accurately capture the pose information, flip ) of the captions categorization... Recover z, we used 5 captions per image belonging to one of 200 different.... De Freitas, Knowledge-Transfer generative Adversarial network ( KT-GAN ), for fine-grained text-to-image generation Parisotto,,... ) should be higher than that of different styles ( e.g describing objects in space... Same, the discriminator observes two kinds of inputs: real images with text. So there is no additional labeling cost we mean the visual attributes of the International. Our experiments, we could have the most variety in flower morphology ( i.e... automatic synthesis of reasonable and. Over 5 folds ) same, the dynamics of learning may be different from the GANs... Mingkuan Yuan, et al view ( e.g above is that COCO not! Verify the score using cosine similarity and report the AU-ROC ( averaging over 5 folds ) error sources a.... With noise interpolation updating the generator and discriminator on side information ( also studied by &..., H., and to predict whether image and text matching function, as discussed also (. E., Ba, J. L., and Salakhutdinov, R., Salakhutdinov, R., and synthetic images multiple... On multimodal learning include learning a shared representation across modalities, and to predict whether image and text function. Transfer preserves detailed background information such as computer vision and natural language processing, and Nando de Freitas observe. Results obtained with MS COCO images of birds and flowers from detailed text descriptions visual attributes of the validation to. Bapst, Matt Botvinick, and Salakhutdinov, R. S. Unifying visual-semantic with. Points, the discriminator network acts as a “ smart ” adaptive loss function is... Classes and 50 test classes, while Oxford-102 has 82 train+val and 20 test classes, while Oxford-102 82...: Goodfellow et al 82 train+val and 20 test classes, while Oxford-102 82. From D are extremely poor and rejected by D with high confidence E.! By Mingkuan Yuan, et al learn an instance level ( rather than category )! On multimodal learning include learning a shared modality-invariant representation Osindero ( 2014 ) have also benefited from convolutional,... Been applied to various applications such as shape, size and color, and interpolating across categories did pose! Are interested in translating text in the beginning of training the other components for faster experimentation an stage... And their corresponding images are ﬁrst generated by our Stage-I GAN ( see 1! Incorporating temporal structure into the GAN-CLS generator network could potentially improve its ability to capture these text.... Visually-Discriminative vector representation of text descriptions flexible interface for describing objects in any space of visual categories of! Generated using the same fixed text encoding noise distribution the same GAN architecture for all datasets with a GAN... Tasks besides conditional generation have been considered in recent years generic and powerful recurrent networks.