Learning From Noisy Large-Scale Datasets With Minimal Supervision
Abstract
We present an approach to effectively utilize small sets of reliable labels in conjunction with massive datasets of noisy labels to learn powerful image representations. A common approach is to pre-train a network using the large set of noisy labels and fine-tune it using the clean labels. We present an alternative: we use the clean labels to captures the structure in the label space and learn a mapping
between noisy and clean labels. This allows to ”clean the dataset”, and fine-tune the network using both the clean labels and the full dataset with reduced noise. The approach comprises a multi-task network that jointly learns to clean noisy labels and to annotate images with accurate labels. We evaluate our approach using the recently released Open Images dataset, containing ∼ 9 million images with multiple annotations per image. Our results demonstrate that the proposed approach outperforms fine-tuning across all major groups of labels in the Open Image dataset. The approach is particularly effective on the large number of labels with 20-80% label noise.
between noisy and clean labels. This allows to ”clean the dataset”, and fine-tune the network using both the clean labels and the full dataset with reduced noise. The approach comprises a multi-task network that jointly learns to clean noisy labels and to annotate images with accurate labels. We evaluate our approach using the recently released Open Images dataset, containing ∼ 9 million images with multiple annotations per image. Our results demonstrate that the proposed approach outperforms fine-tuning across all major groups of labels in the Open Image dataset. The approach is particularly effective on the large number of labels with 20-80% label noise.
Research Areas
