The permutation feature importance is defined to be the decrease in a model score when a single feature value is. The reason we call this a permuted index is that each index entry occurs in multiple (cyclic) permutations, allowing each significant word in every. This is especially useful for non-linear or opaque estimators. Due to its extensive use of attention, it is robust onĪrbitrarily-oriented text which is common in real-world images. Permutation feature importance is a model inspection technique that can be used for any fitted estimator when the data is tabular. PARSeq is optimal on accuracy vs parameter count,įLOPS, and latency because of its simple, unified structure and parallel token It establishes new SOTA results (96.0% accuracy) Using synthetic training data, PARSeqĪchieves state-of-the-art (SOTA) results in STR benchmarks (91.9% accuracy) and Unifies context-free non-AR and context-aware AR inference, and iterative In combinatorics, a permutation is an ordering of a list of objects. Of internal AR LMs with shared weights using Permutation Language Modeling. Permuteexpr, gr returns the list of permuted forms. The input image may cause it to erroneously rectify correct predictions, Permuteexpr, perm permutes the positions of the elements of expr according to the permutation perm. The conditional independence of the external LM on In this hybrid, certain low discrepancy point sets and sequences due to Faure, Niederreiter and Sobol are obtained and their digits are randomly permuted. Inherent limitations of AR models motivated two-stage methods
PERMUTED PDF
Download a PDF of the paper titled Scene Text Recognition with Permuted Autoregressive Sequence Models, by Darwin Bautista and 1 other authors Download PDF Abstract: Context-aware STR methods typically use internal autoregressive (AR) language At that time 50 used permuted blocks, 7 minimisation, 9 simple randomisation and the method used was unclear in 34.
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