Name
Title | ||
|---|---|---|
Deep Convolutional Neural Network with Independent Softmax for Large Scale Face Recognition. |
Abstract | ||
|---|---|---|
In this paper, we present our solution to the MS-Celeb-1M Challenge. This challenge aims to recognize 100k celebrities at the same time. The huge number of celebrities is the bottleneck for training a deep convolutional neural network of which the output is equal to the number of celebrities. To solve this problem, an independent softmax model is proposed to split the single classifier into several small classifiers. Meanwhile, the training data are split into several partitions. This decomposes the large scale training procedure into several medium training procedures which can be solved separately. Besides, a large model is also trained and a simple strategy is introduced to merge the two models. Extensive experiments on the MSR-Celeb-1M dataset demonstrate the superiority of the proposed method. Our solution ranks the first and second in two tracks of the final evaluation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2964284.2984060 | ACM Multimedia |
Field | DocType | Citations |
Training set,Bottleneck,Facial recognition system,Softmax function,Computer science,Convolutional neural network,Artificial intelligence,Classifier (linguistics),Merge (version control),Machine learning | Conference | 8 |
PageRank | References | Authors |
0.47 | 12 | 4 |