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Found 9 papers, 3 papers with code
Consistent algorithms for multi-label classification with macro-at-$k$ metrics
We consider the optimization of complex performance metrics in multi-label classification under the population utility framework.
Generalized test utilities for long-tail performance in extreme multi-label classification
As such, it is characterized by long-tail labels, i. e., most labels have very few positive instances.
Towards Memory-Efficient Training for Extremely Large Output Spaces -- Learning with 500k Labels on a Single Commodity GPU
In classification problems with large output spaces (up to millions of labels), the last layer can require an enormous amount of memory.
Generating artificial digital image correlation data using physics-guided adversarial networks
Digital image correlation (DIC) has become a valuable tool to monitor and evaluate mechanical experiments of cracked specimen, but the automatic detection of cracks is often difficult due to inherent noise and artefacts.
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
We thus propose CascadeXML, an end-to-end multi-resolution learning pipeline, which can harness the multi-layered architecture of a transformer model for attending to different label resolutions with separate feature representations.
Extreme Multi-Label Classification
Multi Label Text Classification
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On Missing Labels, Long-tails and Propensities in Extreme Multi-label Classification
The propensity model introduced by Jain et al. 2016 has become a standard approach for dealing with missing and long-tail labels in extreme multi-label classification (XMLC).
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization
We want to start in a region of weight space a) with low loss value, b) that is favourable for second-order optimization, and c) where the conjugate-gradient (CG) calculations can be performed quickly.
Unbiased Loss Functions for Multilabel Classification with Missing Labels
This paper considers binary and multilabel classification problems in a setting where labels are missing independently and with a known rate.
Unbiased Loss Functions for Extreme Classification With Missing Labels
In addition to the computational burden arising from large number of training instances, features and labels, problems in XMC are faced with two statistical challenges, (i) large number of 'tail-labels' -- those which occur very infrequently, and (ii) missing labels as it is virtually impossible to manually assign every relevant label to an instance.
