RangeAugment: Efficient Online Augmentation with Range Learning
- URL: http://arxiv.org/abs/2212.10553v1
- Date: Tue, 20 Dec 2022 18:55:54 GMT
- Title: RangeAugment: Efficient Online Augmentation with Range Learning
- Authors: Sachin Mehta and Saeid Naderiparizi and Fartash Faghri and Maxwell
Horton and Lailin Chen and Ali Farhadi and Oncel Tuzel and Mohammad Rastegari
- Abstract summary: RangeAugment efficiently learns the range of magnitudes for individual as well as composite augmentation operations.
We show that RangeAugment achieves competitive performance to state-of-the-art automatic augmentation methods with 4-5 times fewer augmentation operations.
- Score: 54.61514286212455
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: State-of-the-art automatic augmentation methods (e.g., AutoAugment and
RandAugment) for visual recognition tasks diversify training data using a large
set of augmentation operations. The range of magnitudes of many augmentation
operations (e.g., brightness and contrast) is continuous. Therefore, to make
search computationally tractable, these methods use fixed and manually-defined
magnitude ranges for each operation, which may lead to sub-optimal policies. To
answer the open question on the importance of magnitude ranges for each
augmentation operation, we introduce RangeAugment that allows us to efficiently
learn the range of magnitudes for individual as well as composite augmentation
operations. RangeAugment uses an auxiliary loss based on image similarity as a
measure to control the range of magnitudes of augmentation operations. As a
result, RangeAugment has a single scalar parameter for search, image
similarity, which we simply optimize via linear search. RangeAugment integrates
seamlessly with any model and learns model- and task-specific augmentation
policies. With extensive experiments on the ImageNet dataset across different
networks, we show that RangeAugment achieves competitive performance to
state-of-the-art automatic augmentation methods with 4-5 times fewer
augmentation operations. Experimental results on semantic segmentation, object
detection, foundation models, and knowledge distillation further shows
RangeAugment's effectiveness.
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