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Found 13 papers, 6 papers with code
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
For the first issue, the more zoomed (telephoto) image can be naturally leveraged as the reference to guide the SR of the lesser zoomed (short-focus) image.
Learning RAW-to-sRGB Mappings with Inaccurately Aligned Supervision
To diminish the effect of color inconsistency in image alignment, we introduce to use a global color mapping (GCM) module to generate an initial sRGB image given the input raw image, which can keep the spatial location of the pixels unchanged, and the target sRGB image is utilized to guide GCM for converting the color towards it.
Ex uno plures: Splitting One Model into an Ensemble of Subnetworks
We show that the proposed subnetwork ensembling method can perform as well as standard deep ensembles in both accuracy and uncertainty estimates, yet with a computational efficiency similar to MC dropout.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
Real-Time Uncertainty Estimation in Computer Vision via Uncertainty-Aware Distribution Distillation
We propose a simple, easy-to-optimize distillation method for learning the conditional predictive distribution of a pre-trained dropout model for fast, sample-free uncertainty estimation in computer vision tasks.
Self-Distillation as Instance-Specific Label Smoothing
It has been recently demonstrated that multi-generational self-distillation can improve generalization.
Two-Dimensional Semi-Nonnegative Matrix Factorization for Clustering
In particular, projection matrices are sought under the guidance of building new data representations, such that the spatial information is retained and projections are enhanced by the goal of clustering, which helps construct optimal projection directions.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
Deep Adaptive Inference Networks for Single Image Super-Resolution
Nonetheless, content and resource adaptive model is more preferred, and it is encouraging to apply simpler and efficient networks to the easier regions with less details and the scenarios with restricted efficiency constraints.
Omnibus Dropout for Improving The Probabilistic Classification Outputs of ConvNets
Motivated by this, we explore the use of various structured dropout techniques to promote model diversity and improve the quality of probabilistic predictions.
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout
Motivated by this, we explore the use of structured dropout to promote model diversity and improve confidence calibration.
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
Here, we present a theoretically grounded set of noise-robust loss functions that can be seen as a generalization of MAE and CCE.
Ranked #14 on
Learning with noisy labels
on CIFAR-10N-Worst
