Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation
arXiv:1805.07509
Abstract
Recent Image-to-Image Translation algorithms have achieved significant progress in neural style transfer and image attribute manipulation tasks. However, existing approaches require exhaustively labelling training data, which is labor demanding, difficult to scale up, and hard to migrate into new domains. To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled. Using a novel one-input multi-output architecture, SG-GAN is well-suited for tackling sparsely grouped learning and multi-task learning. The proposed model can translate images among multiple groups using only a single commonly trained model. To experimentally validate advantages of the new model, we apply the proposed method to tackle a series of attribute manipulation tasks for facial images. Experimental results demonstrate that SG-GAN can generate image translation results of comparable quality with baselines methods on adequately labelled datasets and results of superior quality on sparsely grouped datasets. The official implementation is publicly available:https://github.com/zhangqianhui/Sparsely-Grouped-GAN.
arXiv commentAccepted by ACMMM2018
References in corpus (25)
- Adam: A Method for Stochastic Optimization
- Conditional Generative Adversarial Nets
- Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- Spectral Normalization for Generative Adversarial Networks
- Instance Normalization: The Missing Ingredient for Fast Stylization
- Conditional Image Generation with PixelCNN Decoders
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- Improved Training of Wasserstein GANs
- Improved Techniques for Training GANs
- Adversarially Learned Inference
- Autoencoding beyond pixels using a learned similarity metric
- Invertible Conditional GANs for image editing
- Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
- High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- Multimodal Unsupervised Image-to-Image Translation
- Neural Photo Editing with Introspective Adversarial Networks
- Good Semi-supervised Learning that Requires a Bad GAN
- Deep Identity-aware Transfer of Facial Attributes
- Attention to Scale: Scale-aware Semantic Image Segmentation
- Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for Visual Question Answering
- Unsupervised Image-to-Image Translation with Generative Adversarial Networks
- Neural Face Editing with Intrinsic Image Disentangling
- ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributes
- Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis