Representation Learning

The ImageNet dataset contains 14,197,122 annotated images according to the WordNet hierarchy. Since 2010 the dataset is used in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), a benchmark in image classification and object detection. The publicly released dataset contains a set of manually annotated training images. A set of test images is also released, with the manual annotations withheld. ILSVRC annotations fall into one of two categories: (1) image-level annotation of a binary label for the presence or absence of an object class in the image, e.g., “there are cars in this image” but “there are no tigers,” and (2) object-level annotation of a tight bounding box and class label around an object instance in the image, e.g., “there is a screwdriver centered at position (20,25) with width of 50 pixels and height of 30 pixels”. The ImageNet project does not own the copyright of the images, therefore only thumbnails and URLs of images are provided. * Total number of non-empty WordNet synsets: 21841 * Total number of images: 14197122 * Number of images with bounding box annotations: 1,034,908 * Number of synsets with SIFT features: 1000 * Number of images with SIFT features: 1.2 million Source: ImageNet Large Scale Visual Recognition Challenge Image Source: https://cs.stanford.edu/people/karpathy/cnnembed/

The Reddit dataset is a graph dataset from Reddit posts made in the month of September, 2014. The node label in this case is the community, or “subreddit”, that a post belongs to. 50 large communities have been sampled to build a post-to-post graph, connecting posts if the same user comments on both. In total this dataset contains 232,965 posts with an average degree of 492. The first 20 days are used for training and the remaining days for testing (with 30% used for validation). For features, off-the-shelf 300-dimensional GloVe CommonCrawl word vectors are used. Source: https://arxiv.org/pdf/1706.02216.pdf Image Source: https://minimaxir.com/2016/05/reddit-graph/

The FB15k dataset contains knowledge base relation triples and textual mentions of Freebase entity pairs. It has a total of 592,213 triplets with 14,951 entities and 1,345 relationships. FB15K-237 is a variant of the original dataset where inverse relations are removed, since it was found that a large number of test triplets could be obtained by inverting triplets in the training set. Source: https://www.microsoft.com/en-us/download/details.aspx?id=52312 Image Source: http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf

IMDB-BINARY is a movie collaboration dataset that consists of the ego-networks of 1,000 actors/actresses who played roles in movies in IMDB. In each graph, nodes represent actors/actress, and there is an edge between them if they appear in the same movie. These graphs are derived from the Action and Romance genres. Source: A simple yet effective baseline for non-attributed graph classification

dSprites is a dataset of 2D shapes procedurally generated from 6 ground truth independent latent factors. These factors are color, shape, scale, rotation, x and y positions of a sprite. All possible combinations of these latents are present exactly once, generating N = 737280 total images. Source: dSprites

REDDIT-BINARY consists of graphs corresponding to online discussions on Reddit. In each graph, nodes represent users, and there is an edge between them if at least one of them respond to the other’s comment. There are four popular subreddits, namely, IAmA, AskReddit, TrollXChromosomes, and atheism. IAmA and AskReddit are two question/answer based subreddits, and TrollXChromosomes and atheism are two discussion-based subreddits. A graph is labeled according to whether it belongs to a question/answer-based community or a discussion-based community. Source: A simple yet effective baseline for non-attributed graph classification

The smallNORB dataset is a datset for 3D object recognition from shape. It contains images of 50 toys belonging to 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. The objects were imaged by two cameras under 6 lighting conditions, 9 elevations (30 to 70 degrees every 5 degrees), and 18 azimuths (0 to 340 every 20 degrees). The training set is composed of 5 instances of each category (instances 4, 6, 7, 8 and 9), and the test set of the remaining 5 instances (instances 0, 1, 2, 3, and 5). Source: https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/ Image Source: https://www.kaggle.com/nepuerto/the-small-norb-dataset-v10

WikiMatrix is a dataset of parallel sentences in the textual content of Wikipedia for all possible language pairs. The mined data consists of: - 85 different languages, 1620 language pairs - 134M parallel sentences, out of which 34M are aligned with English Source: WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia

CoMA contains 17,794 meshes of the human face in various expressions Source: DEMEA: Deep Mesh Autoencoders for Non-Rigidly Deforming Objects Image Source: https://coma.is.tue.mpg.de/

Contains 51,583 descriptions of 11,046 objects from 800 ScanNet scenes. ScanRefer is the first large-scale effort to perform object localization via natural language expression directly in 3D. Source: ScanRefer: 3D Object Localization in RGB-D Scans using Natural Language

Sentence Compression is a dataset where the syntactic trees of the compressions are subtrees of their uncompressed counterparts, and hence where supervised systems which require a structural alignment between the input and output can be successfully trained. Source: Overcoming the Lack of Parallel Data in Sentence Compression

SciDocs evaluation framework consists of a suite of evaluation tasks designed for document-level tasks. Source: Allen Institute for AI

The Sprites dataset contains 60 pixel color images of animated characters (sprites). There are 672 sprites, 500 for training, 100 for testing and 72 for validation. Each sprite has 20 animations and 178 images, so the full dataset has 120K images in total. There are many changes in the appearance of the sprites, they differ in their body shape, gender, hair, armor, arm type, greaves, and weapon. Source: Challenges in Disentangling Independent Factors of Variation

For understanding multimodal language used in expressing humor. Source: UR-FUNNY: A Multimodal Language Dataset for Understanding Humor

The BIOSSES data set comprises total 100 sentence pairs all of which were selected from the "TAC2 Biomedical Summarization Track Training Data Set" . The sentence pairs were evaluated by five different human experts that judged their similarity and gave scores in a range [0-4]. Our guideline was prepared based on SemEval 2012 Task 6 Guideline. Image source: BIOSSES

Tatoeba is a free collection of example sentences with translations geared towards foreign language learners. It is available in more than 400 languages. Its name comes from the Japanese phrase “tatoeba” (例えば), meaning “for example”. It is written and maintained by a community of volunteers through a model of open collaboration. Individual contributors are known as Tatoebans.

3dshapes is a dataset of 3D shapes procedurally generated from 6 ground truth independent latent factors. These factors are floor colour, wall colour, object colour, scale, shape and orientation. Source: 3D Shapes Dataset

Omni-Realm Benchmark (OmniBenchmark) is a diverse (21 semantic realm-wise datasets) and concise (realm-wise datasets have no concepts overlapping) benchmark for evaluating pre-trained model generalization across semantic super-concepts/realms, e.g. across mammals to aircraft. [ECCV2022]

Car CAD models from "3d object detection and viewpoint estimation with a deformable 3d cuboid model" were used to generate the dataset. For each of the 199 car models, the authors generated $64\times64$ color renderings from 24 rotation angles each offset by 15 degrees, as well as from 4 different camera elevations.

Paraphrase and Semantic Similarity in Twitter (PIT) presents a constructed Twitter Paraphrase Corpus that contains 18,762 sentence pairs. Source: SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT)

The objective in extreme multi-label classification is to learn feature architectures and classifiers that can automatically tag a data point with the most relevant subset of labels from an extremely large label set. This repository provides resources that can be used for evaluating the performance of extreme multi-label algorithms including datasets, code, and metrics. For more details please visit the link http://manikvarma.org/downloads/XC/XMLRepository.html

Opusparcus is a paraphrase corpus for six European languages: German, English, Finnish, French, Russian, and Swedish. The paraphrases are extracted from the OpenSubtitles2016 corpus, which contains subtitles from movies and TV shows. For each target language, the Opusparcus data have been partitioned into three types of data sets: training, development and test sets. The training sets are large, consisting of millions of sentence pairs, and have been compiled automatically, with the help of probabilistic ranking functions. The development and test sets consist of sentence pairs that have been annotated manually; each set contains approximately 1000 sentence pairs that have been verified to be acceptable paraphrases by two annotators. Source: Opusparcus

Update on 3DIdent, where we introduce six additional object classes (Hare, Dragon, Cow, Armadillo, Horse, and Head), and impose a causal graph over the latent variables. For further details, see Appendix B in the associated paper (https://arxiv.org/abs/2106.04619).

OPUS is a growing collection of translated texts from the web. In the OPUS project we try to convert and align free online data, to add linguistic annotation, and to provide the community with a publicly available parallel corpus. OPUS is based on open source products and the corpus is also delivered as an open content package. We used several tools to compile the current collection. All pre-processing is done automatically. No manual corrections have been carried out.

A data-set which consists of over one million images of physical 3D objects with seven factors of variation, such as object color, shape, size and position. Source: On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset

The Video2GIF dataset contains over 100,000 pairs of GIFs and their source videos. The GIFs were collected from two popular GIF websites (makeagif.com, gifsoup.com) and the corresponding source videos were collected from YouTube in Summer 2015. IDs and URLs of the GIFs and the videos are provided, along with temporal alignment of GIF segments to their source videos. The dataset shall be used to evaluate GIF creation and video highlight techniques. In addition to the 100K GIF-video pairs, the dataset contains 357 pairs of GIFs and their source videos as the test set. The 357 videos come with a Creative Commons CC-BY license, which allowed the authors to redistribute the material with appropriate credit to make the results on test set reproducible even when some of the videos become unavailable. Source: Video2GIF

Novel benchmark which features aspects of natural scenes, e.g. a complex 3D object and different lighting conditions, while still providing access to the continuous ground-truth factors. We use the Blender rendering engine to create visually complex 3D images. Each image in the dataset shows a colored 3D object which is located and rotated above a colored ground in a 3D space. Additionally, each scene contains a colored spotlight which is focused on the object and located on a half-circle around the scene. The observations are encoded with an RGB color space, and the spatial resolution is 224x224 pixels. The images are rendered based on a 10-dimensional latent, where: (1) three dimensions describe the XYZ position, (2) three dimensions describe the rotation of the object in Euler angles, (3) two dimensions describe the color of the object and the ground of the scene, respectively, and (4) two dimensions describe the position and color of the spotlight. We use the HSV color space to describe the color of the object and the ground with only one latent each by having the latent factor control the hue value. The training set and test set contain 250,000 and 25,000 observation-latent pairs, respectively, whereby the latents are uniformly sampled from the unit hyperrectangle.

Arxiv ASTRO-PH (Astro Physics) collaboration network is from the e-print arXiv and covers scientific collaborations between authors papers submitted to Astro Physics category. If an author i co-authored a paper with author j, the graph contains a undirected edge from i to j. If the paper is co-authored by k authors this generates a completely connected (sub)graph on k nodes. Source: https://snap.stanford.edu/data/ca-AstroPh.html

The Discovery datasets consists of adjacent sentence pairs (s1,s2) with a discourse marker (y) that occurred at the beginning of s2. They were extracted from the depcc web corpus. Markers prediction can be used in order to train a sentence encoders. Discourse markers can be considered as noisy labels for various semantic tasks, such as entailment (y=therefore), subjectivity analysis (y=personally) or sentiment analysis (y=sadly), similarity (y=similarly), typicality, (y=curiously) ... The specificity of this dataset is the diversity of the markers, since previously used data used only ~10 imbalanced classes. The author of the dataset provide: - a list of the 174 discourse markers - a Base version of the dataset with 1.74 million pairs (10k examples per marker) - a Big version with 3.4 million pairs - a Hard version with 1.74 million pairs where the connective couldn't be predicted with a fastText linear model Source: GitHub

A scholarly data set with publications’ full-text, annotated in-text citations, and links to metadata. The unarXive data set contains * One million papers in plain text * 63 million citation contexts * 39 million reference strings * A citation network of 16 million connections The data is generated from all LaTeX sources on arXiv from 1991–2020/07 and therefore of higher quality than data generated from PDF files. Furthermore, as all citing papers are available in full text, citation contexts of arbitrary size can be extracted. Typical uses of the data set are approaches in * Citation recommendation * Citation context analysis * Reference string parsing The code for generating the data set is publicly available.

In MutualFriends, two agents, A and B, each have a private knowledge base, which contains a list of friends with multiple attributes (e.g., name, school, major, etc.). The agents must chat with each other to find their unique mutual friend. Source: Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings

SemEval 2014 is a collection of datasets used for the Semantic Evaluation (SemEval) workshop, an annual event that focuses on the evaluation and comparison of systems that can analyze diverse semantic phenomena in text. The datasets from SemEval 2014 are used for various tasks, including but not limited to: - Aspect-Based Sentiment Analysis (ABSA): This task is based on laptop and restaurant reviews. It involves identifying the aspects or features mentioned in a review and determining the sentiment expressed towards each aspect. - Text Classification: This task involves classifying text into predefined categories. Sub-tasks include text-scoring, natural language inference, and semantic-similarity-scoring.

KG20C is a Knowledge Graph about high quality papers from 20 top computer science Conferences. It can serve as a standard benchmark dataset in scholarly data analysis for several tasks, including knowledge graph embedding, link prediction, recommendation systems, and question answering . For more information and download, please see the dataset homepage.

This is a catalogue and repository of network datasets with the aim of aiding scientific research. This website is meant to be browsed both by humans and machines alike, and can also be accessed via a convenient JSON API, or via the graph-tool library. The network datasets themselves are available in several machine-readable formats, in particular gt, GraphML, GML and CSV. The upstream origin of each dataset is meant to be as transparent as possible. Each dataset contains its own publicly available extraction and parsing script, accessible via a git repository, which also includes the entire code for this website, released as Free Software under the AGPLv3. Users are encouraged to inspect the entire pipeline from original upstream data publication, downloading, parsing and format conversion. Users are also welcome to report problems or omissions with the datasets, as well as suggest new ones, either by opening an issue, or simply by forking the git repository and proposing a merge request.

• Games dataset containing 100,000 Gameplay Images of 175 Video Games across 10 Sports Genres - AMERICAN FOOTBALL, BASKETBALL, BIKE RACING, CAR RACING, FIGHTING, HOCKEY, SOCCER, TABLE TENNIS, TENNIS. - Hand-curated images to remove menu/transition frames and only include gameplay sequences. - Games are divided into three visual styling categories: RETRO (arcade-style, 1990s and earlier) MODERN (roughly 2000s) PHOTOREAL (roughly late 2010s).