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Prabhakar’s dataset
A fast, scalable, and reliable deghosting method for extreme exposure fusion. -
Tursun’s dataset
An objective deghosting quality metric for HDR images. -
Kalantari’s dataset
Deep high dynamic range imaging of dynamic scenes. -
Hu’s dataset
Frame high dynamic range photography. -
PowerLinear Activation Functions with application to the first layer of CNNs
Convolutional neural networks (CNNs) have become the state-of-the-art tool for dealing with unsolved problems in computer vision and image processing. -
SBU-Timelapse dataset
The dataset is used for shadow removal tasks. -
ISTD+ dataset
The dataset is used for shadow removal tasks. -
SRD dataset
The dataset is used for shadow removal tasks. -
Arbitrary Style Transfer with Structure Enhancement by Combining the Global a...
Arbitrary style transfer generates an artistic image which combines the structure of a content image and the artistic style of the artwork by using only one trained network. -
Koopcon: A new approach towards smarter and less complex learning
The dataset condensation problem involves transforming a large-scale training set X into a smaller synthetic set X'. -
A dataset of multi-illumination images in the wild
A dataset of multi-illumination images in the wild. -
Describing Textures in the Wild
Describing Textures in the Wild dataset contains texture images with varying lighting conditions. -
CIFAR-10-C and ImageNet-C
The dataset used in the paper is CIFAR-10-C and ImageNet-C, which are common corruption benchmarks. -
Real-World Depth of Field Dataset
The dataset consists of real captured scenes with varying exposures, apertures, and focus distances. -
Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field
The dataset consists of four synthetic rendered scenes and four real captured scenes. The synthetic scenes provide control and ground truth for evaluation, while the real... -
Thumbnail Generation Dataset
Thumbnail generation dataset used in the paper for training and testing the proposed model. -
Incomplete MNIST dataset
The dataset consists of images with missing data, where the missing regions are simulated using different techniques such as square, trapezoid, and noise. -
MisConv: Convolutional Neural Networks for Missing Data
Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like... -
Image Inpainting
The CelebAHQ dataset was used with a fixed removal mask located near the image centers [11]. -
ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable T...
The proposed method incorporates a deformable transformation into the descriptors, making them more robust. The SDDH extracts descriptors only on sparse keypoints, which...
