Stochastic Optimization

The CIFAR-100 dataset (Canadian Institute for Advanced Research, 100 classes) is a subset of the Tiny Images dataset and consists of 60000 32x32 color images. The 100 classes in the CIFAR-100 are grouped into 20 superclasses. There are 600 images per class. Each image comes with a "fine" label (the class to which it belongs) and a "coarse" label (the superclass to which it belongs). There are 500 training images and 100 testing images per class. The criteria for deciding whether an image belongs to a class were as follows: * The class name should be high on the list of likely answers to the question “What is in this picture?” * The image should be photo-realistic. Labelers were instructed to reject line drawings. * The image should contain only one prominent instance of the object to which the class refers. * The object may be partially occluded or seen from an unusual viewpoint as long as its identity is still clear to the labeler. Source: https://www.cs.toronto.edu/~kriz/cifar.html Image Source: https://www.cs.toronto.edu/~kriz/cifar.html

The MNIST database (Modified National Institute of Standards and Technology database) is a large collection of handwritten digits. It has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger NIST Special Database 3 (digits written by employees of the United States Census Bureau) and Special Database 1 (digits written by high school students) which contain monochrome images of handwritten digits. The digits have been size-normalized and centered in a fixed-size image. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. Source: http://yann.lecun.com/exdb/mnist/ Image Source: https://en.wikipedia.org/wiki/MNIST_database#/media/File:MnistExamples.png

General Language Understanding Evaluation (GLUE) benchmark is a collection of nine natural language understanding tasks, including single-sentence tasks CoLA and SST-2, similarity and paraphrasing tasks MRPC, STS-B and QQP, and natural language inference tasks MNLI, QNLI, RTE and WNLI. Source: Align, Mask and Select: A Simple Method for Incorporating Commonsense Knowledge into Language Representation Models Image Source: https://gluebenchmark.com/

The English Penn Treebank (PTB) corpus, and in particular the section of the corpus corresponding to the articles of Wall Street Journal (WSJ), is one of the most known and used corpus for the evaluation of models for sequence labelling. The task consists of annotating each word with its Part-of-Speech tag. In the most common split of this corpus, sections from 0 to 18 are used for training (38 219 sentences, 912 344 tokens), sections from 19 to 21 are used for validation (5 527 sentences, 131 768 tokens), and sections from 22 to 24 are used for testing (5 462 sentences, 129 654 tokens). The corpus is also commonly used for character-level and word-level Language Modelling. Source: Seq2Biseq: Bidirectional Output-wise Recurrent Neural Networks for Sequence Modelling Image Source: https://dl.acm.org/doi/10.5555/972470.972475

AG News (AG’s News Corpus) is a subdataset of AG's corpus of news articles constructed by assembling titles and description fields of articles from the 4 largest classes (“World”, “Sports”, “Business”, “Sci/Tech”) of AG’s Corpus. The AG News contains 30,000 training and 1,900 test samples per class. Source: https://arxiv.org/pdf/1509.01626.pdf

The Corpus of Linguistic Acceptability (CoLA) consists of 10657 sentences from 23 linguistics publications, expertly annotated for acceptability (grammaticality) by their original authors. The public version contains 9594 sentences belonging to training and development sets, and excludes 1063 sentences belonging to a held out test set. Source: https://nyu-mll.github.io/CoLA/ Image Source: https://arxiv.org/pdf/1805.12471.pdf

This dataset contains product reviews and metadata from Amazon, including 142.8 million reviews spanning May 1996 - July 2014. This dataset includes reviews (ratings, text, helpfulness votes), product metadata (descriptions, category information, price, brand, and image features), and links (also viewed/also bought graphs).

MNIST8M is derived from the MNIST dataset by applying random deformations and translations to the dataset. Source: Scalable and Sustainable Deep Learningvia Randomized Hashing

MSRA Hands is a dataset for hand tracking. In total 6 subjects' right hands are captured using Intel's Creative Interactive Gesture Camera. Each subject is asked to make various rapid gestures in a 400-frame video sequence. To account for different hand sizes, a global hand model scale is specified for each subject: 1.1, 1.0, 0.9, 0.95, 1.1, 1.0 for subject 1~6, respectively. The camera intrinsic parameters are: principle point = image center(160, 120), focal length = 241.42. The depth image is 320x240, each .bin file stores the depth pixel values in row scanning order, which are 320240 floats. The unit is millimeters. The bin file is binary and needs to be opened with std::ios::binary flag. joint.txt file stores 400 frames x 21 hand joints per frame. Each line has 3 * 21 = 63 floats for 21 3D points in (x, y, z) coordinates. The 21 hand joints are: wrist, index_mcp, index_pip, index_dip, index_tip, middle_mcp, middle_pip, middle_dip, middle_tip, ring_mcp, ring_pip, ring_dip, ring_tip, little_mcp, little_pip, little_dip, little_tip, thumb_mcp, thumb_pip, thumb_dip, thumb_tip. The corresponding *.jpg file is just for visualization of depth and ground truth joints. Source: https://jimmysuen.github.io/txt/cvpr14_MSRAHandTrackingDB_readme.txt Image Source: https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Qian_Realtime_and_Robust_2014_CVPR_paper.pdf

The OpeReid dataset is a person re-identification dataset that consists of 7,413 images of 200 persons. Source: Scalable Metric Learning via Weighted Approximate Rank Component Analysis