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Awesome Learning to Hash
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<p class="lead">A webpage dedicated to the latest research on learning-to-hash, including state-of-the-art deep hashing models, all updated on a weekly basis. Maintained by <a href="http://sjmoran.github.io/">Sean Moran</a>.</p>
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<p style="font-size: 12px">Contact <a href="http://www.seanjmoran.com">Sean Moran</a> about this survey or website.
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<h3 id="conferences-and-workshops">Conferences and Workshops</h3>
<ul>
<li><a href="https://big-ann-benchmarks.com/neurips23.html">Practical Vector Search Challenge 2023</a></li>
<li><a href="http://big-ann-benchmarks.com/index.html#call">Billion-Scale Approximate Nearest Neighbor Search Challenge: NeurIPS’21 competition track</a></li>
<li><a href="http://www.ee.oulu.fi/~lili/CEFRLatICCV2019.html">Compact and Efficient Feature Representation and Learning in Computer Vision, ICCV 2019</a>: a workshop dedicated to compact feature learning, including binary hashing methods.</li>
<li><a href="http://www.sisap.org/2020/">International Conference on Similarity Search and Applications</a>: a conference dedicated to similarity search</li>
<li><a href="https://workshop-edlcv.github.io/">Joint Workshop on Efficient Deep Learning in Computer Vision</a>: co-located with the CVPR 2020 conference</li>
</ul>
<h3 id="introductory-video-material">Introductory Video Material</h3>
<p>Please also see the excellent <a href="https://cs.nju.edu.cn/lwj/slides/L2H.pdf">tutorial slides</a> of <a href="https://cs.nju.edu.cn/lwj/">Dr. Wu-Jun Li</a> for a nice introduction to the field.</p>
<p><a href="https://www.youtube.com/user/victorlavrenko">Dr Victor Lavrenko</a> has two excellent youtube videos <a href="https://www.youtube.com/watch?v=Arni-zkqMBA">here</a> and <a href="https://www.youtube.com/watch?v=dgH0NP8Qxa8">here</a>, describing the basics of locality sensitive hashing (LSH). These are ideal for those just entering the field or curious how to apply the method to their problem domain.</p>
<p><iframe width="560" height="315" src="https://www.youtube.com/embed/Arni-zkqMBA" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
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<p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/dgH0NP8Qxa8" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
</p>
<p>The 2017 Rice Machine Learning Workshop: Hashing Algorithms for Large-Scale Machine Learning:</p>
<p>
<iframe width="560" height="315" src="https://www.youtube.com/embed/tQ0OJXowLJA" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen=""></iframe>
</p>
<h3 id="survey-papers">Survey Papers</h3>
<p>The reader is encouraged to further explore the following survey publications:</p>
<ul>
<li><a href="https://arxiv.org/pdf/1509.05472.pdf">Learning to Hash for Indexing Big Data - A Survey</a></li>
<li><a href="https://arxiv.org/pdf/1606.00185.pdf">A Survey on Learning to Hash</a></li>
<li><a href="http://www.cs.utexas.edu/~grauman/temp/GraumanFergus_Hashing_chapterdraft.pdf">Learning Binary Hash Codes for Large-Scale Image Search</a></li>
<li><a href="https://www.slaney.org/malcolm/yahoo/Slaney2008-LSHTutorial.pdf">Locality-Sensitive Hashing for Finding Nearest Neighbors</a></li>
</ul>
<h3 id="pre-processed-datasets-for-download">Pre-Processed Datasets for Download</h3>
<ul>
<li><a href="https://www.dropbox.com/s/875u1rkva9iffpj/Gist512CIFAR10.mat?dl=0">CIFAR-10 Gist Features (.mat format)</a></li>
<li><a href="https://www.dropbox.com/s/dwixb9ry4zwvcp9/LabelMe_gist.mat?dl=0">LabelMe Gist Features (.mat format)</a></li>
<li><a href="https://www.dropbox.com/s/x3j6ik6kvnw95h3/MNIST_gnd_release.mat?dl=0">MNIST Pixel Features (.mat format)</a></li>
<li><a href="https://www.dropbox.com/s/29f6r7pqevfy2ck/sift1m.mat?dl=0">SIFT 1M Features (.mat format)</a></li>
<li><a href="https://www.dropbox.com/s/wql7m8wuvn9efhj/20Newsgroups.mat?dl=0">20 Newsgroups (.mat format)</a></li>
<li><a href="https://www.dropbox.com/s/qasz8z3sr1pjqog/TDT2.mat?dl=0">TDT2 (.mat format)</a></li>
<li><a href="http://corpus-texmex.irisa.fr/">BIGANN</a> consists of SIFT descriptors applied to images from extracted from a large image dataset.</li>
<li><a href="https://dl.fbaipublicfiles.com/billion-scale-ann-benchmarks/FB_ssnpp_database.u8bin">Facebook SimSearchNet++</a></li>
<li><a href="https://github.com/microsoft/SPTAG/tree/master/datasets/SPACEV1B">Microsoft SPACEV-1B</a></li>
<li><a href="https://research.yandex.com/datasets/biganns">Yandex DEEP-1B</a></li>
<li><a href="https://research.yandex.com/datasets/biganns">Yandex Text-to-Image-1B</a></li>
</ul>
<h3 id="courses">Courses</h3>
<p>A few university courses are been taught covering aspects of machine of efficient computing. Below there are a few that have publicly available material:</p>
<ul>
<li><a href="http://www.inf.ed.ac.uk/teaching/courses/exc/index_17-18.html">Extreme Computing</a> in University of Edinburgh.</li>
<li><a href="https://www.inf.ed.ac.uk/teaching/courses/tts/">Text Technologies for Data Science</a> in University of Edinburgh.</li>
</ul>
<h3 id="blog-posts">Blog Posts</h3>
<p>Blog posts provide a great way to learn about cutting edge research and ideas. Here are a few of our favourites:</p>
<ul>
<li><a href="https://medium.com/@sean.j.moran/learning-to-hash-finding-the-needle-in-the-haystack-with-ai-24a15f85de0e">Learning to Hash — Finding the Needle in the HayStack with AI</a></li>
<li><a href="https://towardsdatascience.com/fast-near-duplicate-image-search-using-locality-sensitive-hashing-d4c16058efcb">Fast Near-Duplicate Image Search using Locality Sensitive Hashing</a></li>
<li><a href="https://blog.bradfieldcs.com/an-introduction-to-hashing-in-the-era-of-machine-learning-6039394549b0">An Introduction to Hashing in the Era of Machine Learning</a></li>
<li><a href="https://towardsdatascience.com/understanding-locality-sensitive-hashing-49f6d1f6134">Locality Sensitive Hashing: An effective way of reducing the dimensionality of your data</a></li>
<li><a href="https://www.wikiwand.com/en/Johnson%E2%80%93Lindenstrauss_lemma">Johnson–Lindenstrauss lemma</a></li>
<li><a href="http://rakaposhi.eas.asu.edu/s01-cse494-mailarchive/msg00054.html">LSH Ideas</a></li>
<li><a href="http://tylerneylon.com/a/lsh1/">Introduction to Locality-Sensitive Hashing (nice visualisations!)</a></li>
<li><a href="https://www.quora.com/What-is-locality-sensitive-hashing">What is locality-sensitive hashing?</a></li>
</ul>
<h3 id="hashing-software-packages">Hashing Software Packages</h3>
<ul>
<li><a href="https://github.com/thulab/DeepHash">Deep Hashing Toolbox</a></li>
</ul>
<h3 id="books">Books</h3>
<p>Some favourite books on the general topic of large-scale machine learning:</p>
<ul>
<li>
<p><a href="http://www.mmds.org/">Mining of Massive Datasets</a>: great content throughout on all sorts of large-scale data mining topics from Hadoop to Google AdWords. Book includes a detailed treatment of LSH.</p>
</li>
<li>
<p><a href="https://nlp.stanford.edu/IR-book/information-retrieval-book.html">Introduction to Information Retrieval</a>: arguably a classic book on information retrieval basics, very well-written, with a comprehensive overview of data indexing and retrieval.</p>
</li>
</ul>
<p>Please, feel free to submit a pull request to adding more links in this page.</p>
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