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.
TASK2VEC CODE
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 the best available feature extractor, while costing substantially less thanĮxhaustively training and evaluating on all available feature extractors. Selecting a feature extractor with task embedding obtains a performance close That is capable of predicting which feature extractors will perform well. Present a simple meta-learning framework for learning a metric on embeddings Meta-task of selecting a pre-trained feature extractor for a new task.
Similar) We also demonstrate the practical value of this framework for the Visual tasks (e.g., tasks based on classifying different types of plants are Match our intuition about semantic and taxonomic relations between different Weĭemonstrate that this embedding is capable of predicting task similarities that This provides a fixed-dimensionalĮmbedding of the task that is independent of details such as the number ofĬlasses and does not require any understanding of the class label semantics. Given a dataset with ground-truth labels and a lossįunction defined over those labels, we process images through a "probe network"Īnd compute an embedding based on estimates of the Fisher information matrixĪssociated with the probe network parameters. We introduce a method to provide vectorial representations of visualĬlassification tasks which can be used to reason about the nature of those Task2Vec: Task Embedding for Meta-Learning
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