Dimensionality Reduction

EMNIST (extended MNIST) has 4 times more data than MNIST. It is a set of handwritten digits with a 28 x 28 format. Source: Domain Discrepancy Measure for Complex Models in Unsupervised Domain Adaptation Image Source: https://arxiv.org/pdf/1803.01900.pdf

CN-Celeb is a large-scale speaker recognition dataset collected `in the wild'. This dataset contains more than 130,000 utterances from 1,000 Chinese celebrities, and covers 11 different genres in real world. Source: CN-CELEB: a challenging Chinese speaker recognition dataset

Oxford105k is the combination of the Oxford5k dataset and 99782 negative images crawled from Flickr using 145 most popular tags. This dataset is used to evaluate search performance for object retrieval (reported as mAP) on a large scale. Source: Multiple Measurements and Joint Dimensionality Reduction for Large Scale Image Search with Short Vectors Extended Version

STRING is a collection of protein-protein interaction (PPI) networks.

HolStep is a dataset based on higher-order logic (HOL) proofs, for the purpose of developing new machine learning-based theorem-proving strategies. Source: HolStep: A Machine Learning Dataset for Higher-order Logic Theorem Proving

The Oxford-Affine dataset is a small dataset containing 8 scenes with sequence of 6 images per scene. The images in a sequence are related by homographies. Source: A Large Dataset for Improving Patch Matching Image Source: https://www.robots.ox.ac.uk/~vgg/data/affine/

The AtariARI (Atari Annotated RAM Interface) is an environment for representation learning. The Atari Arcade Learning Environment (ALE) does not explicitly expose any ground truth state information. However, ALE does expose the RAM state (128 bytes per timestep) which are used by the game programmer to store important state information such as the location of sprites, the state of the clock, or the current room the agent is in. To extract these variables, the dataset creators consulted commented disassemblies (or source code) of Atari 2600 games which were made available by Engelhardt and Jentzsch and CPUWIZ. The dataset creators were able to find and verify important state variables for a total of 22 games. Once this information was acquired, combining it with the ALE interface produced a wrapper that can automatically output a state label for every example frame generated from the game. The dataset creators make this available with an easy-to-use gym wrapper, which returns this information with no change to existing code using gym interfaces. Source: https://arxiv.org/pdf/1906.08226.pdf Image Source: https://github.com/mila-iqia/atari-representation-learning

GoodSounds dataset contains around 28 hours of recordings of single notes and scales played by 15 different professional musicians, all of them holding a music degree and having some expertise in teaching. 12 different instruments (flute, cello, clarinet, trumpet, violin, alto sax alto, tenor sax, baritone sax, soprano sax, oboe, piccolo and bass) were recorded using one or up to 4 different microphones. For all the instruments the whole set of playable semitones in the instrument is recorded several times with different tonal characteristics. Each note is recorded into a separate monophonic audio file of 48kHz and 32 bits. Rich annotations of the recordings are available, including details on recording environment and rating on tonal qualities of the sound (“good-sound”, “bad”, “scale-good”, “scale-bad”). Source: A real-time system for measuring sound goodness in instrumental sounds Image Source: A real-time system for measuring sound goodness in instrumental sounds Audio Source: https://zenodo.org/record/820937

The Deep Fakes Dataset is a collection of "in the wild" portrait videos for deepfake detection. The videos in the dataset are diverse real-world samples in terms of the source generative model, resolution, compression, illumination, aspect-ratio, frame rate, motion, pose, cosmetics, occlusion, content, and context. They originate from various sources such as news articles, forums, apps, and research presentations; totalling up to 142 videos, 32 minutes, and 17 GBs. Synthetic videos are matched with their original counterparts when possible. Source: Deepfakes dataset

The Specs on Faces (SoF) dataset, a collection of 42,592 (2,662×16) images for 112 persons (66 males and 46 females) who wear glasses under different illumination conditions. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is focused on two challenges: harsh illumination environments and face occlusions, which highly affect face detection, recognition, and classification. The glasses are the common natural occlusion in all images of the dataset. However, there are two more synthetic occlusions (nose and mouth) added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty (easy, medium, and hard). That enlarges the number of images to be 42,592 images (26,112 male images and 16,480 female images). There is metadata for each image that contains many information such as: the subject ID, facial landmarks, face and glasses rectangles, gender and age labels, year that the photo was taken, facial emotion, glasses type, and more. Source: https://sites.google.com/view/sof-dataset Image Source: https://sites.google.com/view/sof-dataset

Repository of a generative art dataset by computer artist Andy Lomas. Source: https://github.com/SensiLab/Andy-Lomas-Generative-Art-Dataset Image Source: https://github.com/SensiLab/Andy-Lomas-Generative-Art-Dataset