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Effective training of convolutional networks using noisy Web images
2015
2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI)
Deep convolutional networks have recently shown very interesting performance in a variety of computer vision tasks. Besides network architecture optimization, a key contribution to their success is the availability of training data. Network training is usually done with manually validated data but this approach has a significant cost and poses a scalability problem. Here we introduce an innovative pipeline that combines weakly-supervised image reranking methods and network finetuning to
doi:10.1109/cbmi.2015.7153607
dblp:conf/cbmi/VoGBP15
fatcat:sejc5m4wabczdeoas7wquivkdu