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DistilNAS: Neural Architecture Search With Distilled Data
Prabhakar, Swaroop N ; Deshwal, Ankur ; Mishra, Rahul ; Kim, Hyeonsu
Access, IEEE, 2022, Vol.10, p.124990-124998
IEEE
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Título:
DistilNAS: Neural Architecture Search With Distilled Data
Autor:
Prabhakar, Swaroop N
;
Deshwal, Ankur
;
Mishra, Rahul
;
Kim, Hyeonsu
Assuntos:
Costs
;
Curriculum development
;
Curriculum learning
;
dataset distillation
;
Feature extraction
;
Focusing
;
Image segmentation
;
Learning systems
;
neural architecture search
;
Object detection
;
Search problems
;
Training data
É parte de:
Access, IEEE, 2022, Vol.10, p.124990-124998
Descrição:
Can we perform Neural Architecture Search (NAS) with a smaller subset of target dataset and still fair better in terms of performance with significant reduction in search cost? In this work, we propose a method, called DistilNAS, which utilizes a curriculum learning based approach to distill the target dataset into a very efficient smaller dataset to perform NAS. We hypothesize that only the data samples containing features highly relevant to a given class should be used in the search phase of the NAS. We perform NAS with a distilled version of dataset and the searched model achieves a better performance with a much reduced search cost in comparison with various baselines. For instance, on Imagenet dataset, the DistilNAS uses only 10% of the training data and produces a model in ≈1 GPU-day (includes the time needed for clustering) that achieves near SOTA accuracy of 75.75% (PC-DARTS had achieved SOTA with an accuracy of 75.8% but needed 3.8 GPU-days for architecture search). We also demonstrate and discuss the efficacy of DistilNAS on several other publicly available datasets.
Editor:
IEEE
Idioma:
Inglês
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