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Fashion Style in 128 Floats: Joint Ranking and Classification Using Weak Data for Feature Extraction

CVPR 2016 · Label-efficient and robust learning › Recognition and long-tail · approach: Learning from unlabeled and positive-unlabeled data · 161 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Jointly train feature extraction with ranking and classification losses128-dimensional fashion features · Hipster Warsaccuracy: 73.5 -> 75.9Helped
Add the classification loss to ranking-only training (ablation)128-dimensional fashion features · Hipster Warsaccuracy: 74.5 -> 75.9Helped
Add the ranking loss to classification-only training (ablation)128-dimensional fashion features · Hipster Warsaccuracy: 73.5 -> 75.9Helped
Clean the training data before joint training (ablation)Fashion144k-trained features · Hipster Warsaccuracy: 72.9 -> 75.9Helped
Jointly train with ranking and classification rather than Siamese loss (ablation)128-dimensional fashion features · Hipster Warsaccuracy: 73.3 -> 75.9Helped
Jointly train feature extraction with ranking and classification losses128-dimensional fashion features · Fashion144kaccuracy: 14.6 -> 17.0Helped
Add the classification loss to Siamese training (ablation)128-dimensional fashion features · Fashion144kaccuracy: 13.9 -> 17.0Helped
Train the proposed architecture instead of using random weights (ablation)128-dimensional fashion features · Fashion144kaccuracy: 13.0 -> 17.0Helped
Use the joint method instead of VGG 16 Layers featuresfashionability prediction · Fashion144kaccuracy: 16.6 -> 17.0Helped

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