LCNME: Label Correction Using Network Prediction Based on Memorization Effects for Cross-Modal Retrieval with Noisy Labels

Cross-modal retrieval with noisy labels has attracted much attention. This state-of-the-art method trains a network to increase weights for clean labels in the loss. However, we have found that the network is eventually overfitted to the remaining noisy labels as training progresses. Motivated by th...

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Veröffentlicht in:	IEEE transactions on circuits and systems for video technology 2024-01, Vol.34 (1), p.1-1
Hauptverfasser:	Okamura, Daiki, Harakawa, Ryosuke, Iwahashi, Masahiro
Format:	Artikel
Sprache:	eng
Schlagworte:	Cross-modal retrieval deep learning Electronic mail Encyclopedias Internet label correction Labels neural network Noise measurement noisy label Predictions Representation learning Retrieval Training
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