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Transferring the knowledge learned from large scale datasets (e.g., ImageNet)via fine-tuning offers an effective solution for domain-specific fine-grainedvisual categorization (FGVC) tasks (e.g., recognizing bird species or car makeand model). In such scenarios, data annotation often calls for specializeddomain knowledge and thus is difficult to scale. In this work, we first tacklea problem in large scale FGVC. Our method won first place in iNaturalist 2017large scale species classification challenge. Central to the success of ourapproach is a training scheme that uses higher image resolution and deals withthe long-tailed distribution of training data. Next, we study transfer learningvia fine-tuning from large scale datasets to small scale, domain-specific FGVCdatasets. We propose a measure to estimate domain similarity via Earth Mover'sDistance and demonstrate that transfer learning benefits from pre-training on asource domain that is similar to the target domain by this measure. Ourproposed transfer learning outperforms ImageNet pre-training and obtainsstate-of-the-art results on multiple commonly used FGVC datasets.

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning