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Dictionary Learning is an important problem in multiple areas, ranging from computational neuroscience, machine learning, to computer vision and image processing. The general goal is to find a good basis for given data. More formally, in the Dictionary Learning problem, also known as sparse coding, we are given samples of a random vector $y\in\mathbb{R}^n$, of the form $y=Ax$ where $A$ is some unknown matrix in $\mathbb{R}^{n×m}$, called dictionary, and $x$ is sampled from an unknown distribution over sparse vectors. The goal is to approximately recover the dictionary $A$.
Source: Polynomial-time tensor decompositions with sum-of-squares
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The Extended Yale B database contains 2414 frontal-face images with size 192×168 over 38 subjects and about 64 images per subject. The images were captured under different lighting conditions and various facial expressions. Source: Learning Locality-Constrained Collaborative Representation for Robust Face Recognition Image Source: http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html
Partial REID is a specially designed partial person reidentification dataset that includes 600 images from 60 people, with 5 full-body images and 5 occluded images per person. These images were collected on a university campus by 6 cameras from different viewpoints, backgrounds and different types of occlusion. The examples of partial persons in the Partial REID dataset are shown in the Figure. Source: Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification Image Source: https://arxiv.org/abs/1810.07399
A novel dataset facilitating multimodal and Synergetic sociAL Scene Analysis. Source: SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
The Fluorescence Microscopy Denoising (FMD) dataset is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real fluorescence microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. Image averaging is used to effectively obtain ground truth images and 60,000 noisy images with different noise levels. Source: https://arxiv.org/abs/1812.10366 Image Source: https://github.com/bmmi/denoising-fluorescence
Partial iLIDS is a dataset for occluded person person re-identification. It contains a total of 476 images of 119 people captured by 4 non-overlapping cameras. Some images contain people occluded by other individuals or luggage. Source: Foreground-aware Pyramid Reconstruction for Alignment-free Occluded Person Re-identification Image Source: https://arxiv.org/pdf/1904.04975.pdf
LoDoPaB-CT is a dataset of computed tomography images and simulated low-dose measurements. It contains over 40,000 scan slices from around 800 patients selected from the LIDC/IDRI Database. Source: The LoDoPaB-CT Dataset: A Benchmark Dataset for Low-Dose CT Reconstruction Methods
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