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Multi-label Iterated Learning for Image Classification with Label Ambiguity
[article]
2021
Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly labeled since images with multiple object classes present are assigned a single label. This ambiguity biases models towards a single prediction, which could result in the suppression of classes that tend to co-occur in the data. Inspired by language emergence literature, we propose multi-label iterated learning (MILe) to
doi:10.48550/arxiv.2111.12172
fatcat:3nqqsh3arjc6bbcodb54cx6yji