Generative Hierarchical Features from Synthesizing Images
arXiv:2007.10379
Abstract
Generative Adversarial Networks (GANs) have recently advanced image synthesis by learning the underlying distribution of the observed data. However, how the features learned from solving the task of image generation are applicable to other vision tasks remains seldom explored. In this work, we show that learning to synthesize images can bring remarkable hierarchical visual features that are generalizable across a wide range of applications. Specifically, we consider the pre-trained StyleGAN generator as a learned loss function and utilize its layer-wise representation to train a novel hierarchical encoder. The visual feature produced by our encoder, termed as Generative Hierarchical Feature (GH-Feat), has strong transferability to both generative and discriminative tasks, including image editing, image harmonization, image classification, face verification, landmark detection, and layout prediction. Extensive qualitative and quantitative experimental results demonstrate the appealing performance of GH-Feat.
arXiv commentCVPR 2021 camera-ready
References in corpus (15)
- Auto-Encoding Variational Bayes
- Representation Learning with Contrastive Predictive Coding
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Large Scale GAN Training for High Fidelity Natural Image Synthesis
- How transferable are features in deep neural networks?
- Learning deep representations by mutual information estimation and maximization
- LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop
- Data-Efficient Image Recognition with Contrastive Predictive Coding
- Adversarially Learned Inference
- Contrastive Multiview Coding
- Large Scale Adversarial Representation Learning
- GAN Dissection: Visualizing and Understanding Generative Adversarial Networks
- Unsupervised Learning of Object Landmarks through Conditional Image Generation
- What makes instance discrimination good for transfer learning?
- Unsupervised Landmark Learning from Unpaired Data