See Better Before Looking Closer: Weakly Supervised Data Augmentation Network for Fine-Grained Visual Classification

26 Jan 2019Tao HuHonggang QiQingming HuangYan Lu

Data augmentation is usually adopted to increase the amount of training data, prevent overfitting and improve the performance of deep models. However, in practice, random data augmentation, such as random image cropping, is low-efficiency and might introduce many uncontrolled background noises... (read more)

PDF Abstract
TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT LEADERBOARD
Fine-Grained Image Classification CUB-200-2011 WS-DAN Accuracy 89.4 # 1
Fine-Grained Image Classification FGVC Aircraft WS-DAN Accuracy 93.0% # 10
Fine-Grained Image Classification Stanford Cars WS-DAN Accuracy 94.5% # 11

Methods used in the Paper


METHOD TYPE
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