Oct 21, 2021 · In this work, we explore the use of dimension reduction techniques as a way to find task-significant features helping to make better predictions.
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Jul 6, 2021 · In this work, we explore the use of dimension reduction techniques as a way to find task-significant features helping to make better predictions ...
We measure the performance of the reduced features by assigning a score based on the intra-class and inter-class distance, and selecting a feature reduction ...
In this work, we explore the use of dimension reduction techniques as a way to find task-significant features. We measure the perfor- mance of the reduced ...
We found that using the support and query data, allowed the feature reduction methods to better interpret the structure of the data, thus obtaining a better ...
Jul 7, 2024 · We measure the performance of the reduced features by assigning a score based on the intra-class and inter-class distance, and selecting a ...
This work explores the use of dimension reduction techniques as a way to find task-significant features helping to make better predictions in few-shot ...
Few-shot learning is a fairly new technique that specialize in problems where we have little amount of data. The goal of this method is to classify ...
LatinX in AI (LXAI) at CVPR 2021: Finding Significant Features for Few-Shot Learning Using Dimensionality Reduction Techniques The workshop is a one-day ...
Mendez-Ruiz M. et al. Finding Significant Features for Few-Shot Learning Using Dimensionality Reduction // Lecture Notes in Computer Science. 2021. pp. 131-142.