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Generative adversarial networks (GANs) successfully generate high quality data by learning a mapping from a latent vector to the data. Various studies assert that the latent space of a GAN is semantically meaningful and can be utilized for advanced data analysis and manipulation. To analyze the real data in the latent space of a GAN, it is necessary to build an inference mapping from the data to the latent vector. This paper proposes an effective algorithm to accurately infer the latent vector by utilizing GAN discriminator features. Our primary goal is to increase inference mapping accuracy with minimal training overhead. Furthermore, using the proposed algorithm, we suggest a conditional image generation algorithm, namely a spatially conditioned GAN. Extensive evaluations confirmed that the proposed inference algorithm achieved more semantically accurate inference mapping than existing methods and can be successfully applied to advanced conditional image generation tasks.

Facial landmark detection is an essential task in face-processing techniques. Traditional methods however require expensive pixel-level labels. Semi-supervised facial landmark detection has been explored as an alternative but previous approaches only focus on training-oriented issues (e.g., noisy pseudolabels in the semi-supervised learning), neglecting task-oriented issues (i.e., the quantization error in the landmark detection). We argue that semi-supervised landmark detectors should resolve the two technical issues simultaneously. Through a simple experiment, we found that task-and training-oriented solutions may negatively influence each other, thus eliminating their negative interactions is important. To this end, we devise a new heatmap regression framework via hybrid representation, namely HybridMatch. We utilize both 1-D and 2-D heatmap representations. Here, the 1-D and 2-D heatmap help alleviate the task-oriented and the training-oriented issues, respectively. To exploit the advantages of our hybrid representation, we introduce curriculum learning; relying more on the 2-D heatmap at the early training stage and gradually increasing the effects of the 1-D heatmap. By resolving the two issues simultaneously, we can capture more precise landmark points than existing methods with only a few annotated data. Extensive experiments show that HybridMatch achieves state-of-the-art performance on three benchmark datasets, especially showing 26.3% NME improvement over the existing method in the 300-W full set at 5% data ratio. Surprisingly, our method records a comparable performance, 5.04 (challenging set in the 300-W) to the fully-supervised facial landmark detector 5.03. The remarkable performance of HybridMatch shows its potential as a practical alternative to the fully-supervised model.

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