![]() Plot is generic seasonal shonen anime blob with lots of implied cruelty and suffering where none of that is actually part of the game so it turns out to be pretty infantile. Below you can see an example.It's playable and can be fun. Then click the "Parse references" button to link references to papers in PapersWithCode and annotate the results. First, you’ll need at least one record in the cell that has results (see image below for an example). How do I add referenced results? If a table has references, you can use the parse references feature to get more results from other papers. When editing multiple results from the same table you can click the "Change all" button to copy the current value to all other records from that table.If you're feeling lucky, Cmd+Click a cell in a table to get the first result automatically.If the benchmark doesn’t exist, a “new” icon will appear signifying a new leaderboard.If a benchmark already exists for a dataset/task pair you enter, you’ll see a link appear.Note that you can use parentheses to highlight details, for example: BERT Large (12 layers), FoveaBox (ResNeXt-101), EfficientNet-B7 (NoisyStudent). What are the model naming conventions? Model name should be straightforward, as presented in the paper. ImageNet on Image Classification already exists with metrics Top 1 Accuracy and Top 5 Accuracy. ![]() You should check if a benchmark already exists to prevent duplication if it doesn’t exist you can create a new dataset. Then choose a task, dataset and metric name from the Papers With Code taxonomy. You can manually edit the incorrect or missing fields. How do I add a new result from a table? Click on a cell in a table on the left hand side where the result comes from. ![]() Help! Don’t worry! If you make mistakes we can revert them: everything is versioned! So just tell us on the Slack channel if you’ve accidentally deleted something (and so on) - it’s not a problem at all, so just go for it! I’m editing for the first time and scared of making mistakes. Where do referenced results come from? If we find referenced results in a table to other papers, we show a parsed reference box that editors can use to annotate to get these extra results from other papers. Where do suggested results come from? We have a machine learning model running in the background that makes suggestions on papers. Blue is a referenced result that originates from a different paper. What do the colors mean? Green means the result is approved and shown on the website. A result consists of a metric value, model name, dataset name and task name. What are the colored boxes on the right hand side? These show results extracted from the paper and linked to tables on the left hand side. It shows extracted results on the right hand side that match the taxonomy on Papers With Code. ![]() What is this page? This page shows tables extracted from arXiv papers on the left-hand side. To our best knowledge, this paper is the first comprehensive survey focusing on finger vein recognition based on artificial neural networks. Finally, the challenges and potential development directions in finger vein recognition are discussed. After that, we summarize the related finger vein recognition tasks based on classical neural networks and deep neural networks, respectively. The public datasets that are widely used in finger vein recognition are then described. Then, the development history of artificial neural networks and the representative networks on finger vein recognition tasks are introduced. First, we introduce the background of finger vein recognition and the motivation of this survey. To summarize the development of finger vein recognition based on artificial neural networks, this paper collects 149 related papers. Unlike the finger vein recognition methods based on traditional machine learning, the artificial neural network technique, especially deep learning, it without relying on feature engineering and have superior performance. They are almost impossible to be stolen and difficult to interfere with by external conditions. Due to this advantage, finger vein recognition is highly stable and private. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Artificial Neural Networks for Finger Vein Recognition: A Surveyįinger vein recognition is an emerging biometric recognition technology. ![]()
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