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We present an approach to effectively use millions of images with noisyannotations in conjunction with a small subset of cleanly-annotated images tolearn powerful image representations. One common approach to combine clean andnoisy data is to first pre-train a network using the large noisy dataset andthen fine-tune with the clean dataset. We show this approach does not fullyleverage the information contained in the clean set. Thus, we demonstrate howto use the clean annotations to reduce the noise in the large dataset beforefine-tuning the network using both the clean set and the full set with reducednoise. The approach comprises a multi-task network that jointly learns to cleannoisy annotations and to accurately classify images. We evaluate our approachon the recently released Open Images dataset, containing ~9 million images,multiple annotations per image and over 6000 unique classes. For the smallclean set of annotations we use a quarter of the validation set with ~40kimages. Our results demonstrate that the proposed approach clearly outperformsdirect fine-tuning across all major categories of classes in the Open Imagedataset. Further, our approach is particularly effective for a large number ofclasses with wide range of noise in annotations (20-80% false positiveannotations).

Learning From Noisy Large-Scale Datasets With Minimal Supervision