Corpus ID: 204509571

Image Generation and Recognition (Emotions)

  title={Image Generation and Recognition (Emotions)},
  author={Hanne Carlsson and Dimitrios Kollias},
Generative Adversarial Networks (GANs) were proposed in 2014 by Goodfellow et al., and have since been extended into multiple computer vision applications. This report provides a thorough survey of recent GAN research, outlining the various architectures and applications, as well as methods for training GANs and dealing with latent space. This is followed by a discussion of potential areas for future GAN research, including: evaluating GANs, better understanding GANs, and techniques for… Expand


Self-Attention Generative Adversarial Networks
The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Expand
Generative Adversarial Text to Image Synthesis
A novel deep architecture and GAN formulation is developed to effectively bridge advances in text and image modeling, translating visual concepts from characters to pixels. Expand
Spectral Normalization for Generative Adversarial Networks
This paper proposes a novel weight normalization technique called spectral normalization to stabilize the training of the discriminator and confirms that spectrally normalized GANs (SN-GANs) is capable of generating images of better or equal quality relative to the previous training stabilization techniques. Expand
Improved Techniques for Training GANs
This work focuses on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic, and presents ImageNet samples with unprecedented resolution and shows that the methods enable the model to learn recognizable features of ImageNet classes. Expand
A Style-Based Generator Architecture for Generative Adversarial Networks
An alternative generator architecture for generative adversarial networks is proposed, borrowing from style transfer literature, that improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation. Expand
Improved Training of Wasserstein GANs
This work proposes an alternative to clipping weights: penalize the norm of gradient of the critic with respect to its input, which performs better than standard WGAN and enables stable training of a wide variety of GAN architectures with almost no hyperparameter tuning. Expand
Progressive Growing of GANs for Improved Quality, Stability, and Variation
A new training methodology for generative adversarial networks is described, starting from a low resolution, and adding new layers that model increasingly fine details as training progresses, allowing for images of unprecedented quality. Expand
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning. Expand
cGANs with Projection Discriminator
With this modification, the quality of the class conditional image generation on ILSVRC2012 (ImageNet) 1000-class image dataset is significantly improved and the application to super-resolution was extended and succeeded in producing highly discriminative super- resolution images. Expand
Neural Photo Editing with Introspective Adversarial Networks
The Neural Photo Editor is presented, an interface that leverages the power of generative neural networks to make large, semantically coherent changes to existing images, and the Introspective Adversarial Network is introduced, a novel hybridization of the VAE and GAN. Expand