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Found 9 papers, 7 papers with code
Boosting Flow-based Generative Super-Resolution Models via Learned Prior
This prior is a latent code predicted by our proposed latent module conditioned on the low-resolution image, which is then transformed by the flow model into an SR image.
Ranked #3 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(using extra training data)
Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow.
Ranked #5 on
Image Super-Resolution
on DIV2K val - 4x upscaling
(using extra training data)
ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
Despite their effectiveness, using depth as domain invariant information in UDA tasks may lead to multiple issues, such as excessively high extraction costs and difficulties in achieving a reliable prediction quality.
Denoising Likelihood Score Matching for Conditional Score-based Data Generation
These methods facilitate the training procedure of conditional score models, as a mixture of scores can be separately estimated using a score model and a classifier.
Ranked #1 on
Conditional Image Generation
on CIFAR-100
CLCC: Contrastive Learning for Color Constancy
In this paper, we present CLCC, a novel contrastive learning framework for color constancy.
COCO-GAN: Conditional Coordinate Generative Adversarial Network
The fact that the patch generation process is independent to each other inspires a wide range of new applications: firstly, "Patch-Inspired Image Generation" enables us to generate the entire image based on a single patch.
COCO-GAN: Generation by Parts via Conditional Coordinating
On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand.
Ranked #1 on
Image Generation
on CelebA-HQ 64x64
Knowledge Distillation with Feature Maps for Image Classification
One promising method is knowledge distillation (KD), which creates a fast-to-execute student model to mimic a large teacher network.
Escaping from Collapsing Modes in a Constrained Space
Generative adversarial networks (GANs) often suffer from unpredictable mode-collapsing during training.
Ranked #29 on
Image Generation
on CelebA 64x64
