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Found 8 papers, 2 papers with code
The GIST and RIST of Iterative Self-Training for Semi-Supervised Segmentation
We further show that GIST and RIST can be combined with existing semi-supervised learning methods to boost performance.
Semantic Relation Preserving Knowledge Distillation for Image-to-Image Translation
Generative adversarial networks (GANs) have shown significant potential in modeling high dimensional distributions of image data, especially on image-to-image translation tasks.
Continuous Face Aging via Self-estimated Residual Age Embedding
In this work, we propose a unified network structure that embeds a linear age estimator into a GAN-based model, where the embedded age estimator is trained jointly with the encoder and decoder to estimate the age of a face image and provide a personalized target age embedding for age progression/regression.
Deep Graphics Encoder for Real-Time Video Makeup Synthesis from Example
While makeup virtual-try-on is now widespread, parametrizing a computer graphics rendering engine for synthesizing images of a given cosmetics product remains a challenging task.
Real-time Virtual-Try-On from a Single Example Image through Deep Inverse Graphics and Learned Differentiable Renderers
In this paper we propose a novel framework based on deep learning to build a real-time inverse graphics encoder that learns to map a single example image into the parameter space of a given augmented reality rendering engine.
Exploring Gradient-based Multi-directional Controls in GANs
Generative Adversarial Networks (GANs) have been widely applied in modeling diverse image distributions.
Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers
Equipped with the learned unstructured attention pattern, sparse attention ViT (Sparsifiner) produces a superior Pareto-optimal trade-off between FLOPs and top-1 accuracy on ImageNet compared to token sparsity.
SC2GAN: Rethinking Entanglement by Self-correcting Correlated GAN Space
We propose a novel framework SC$^2$GAN that achieves disentanglement by re-projecting low-density latent code samples in the original latent space and correcting the editing directions based on both the high-density and low-density regions.
