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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning

February 1, 2023

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Authors

Lanqing He

Tsinghua University


Zhongdao Wang

Tsinghua University


Yali Li

Tsinghua University


Shengjin Wang

Tsinghua University


DOI:

10.1609/aaai.v34i07.6729


Abstract:

The softmax loss and its variants are widely used as objectives for embedding learning applications like face recognition. However, the intra- and inter-class objectives in Softmax are entangled, therefore a well-optimized inter-class objective leads to relaxation on the intra-class objective, and vice versa. In this paper, we propose to dissect Softmax into independent intra- and inter-class objective (D-Softmax) with a clear understanding. It is straightforward to tune each part to the best state with D-Softmax as objective.Furthermore, we find the computation of the inter-class part is redundant and propose sampling-based variants of D-Softmax to reduce the computation cost. The face recognition experiments on regular-scale data show D-Softmax is favorably comparable to existing losses such as SphereFace and ArcFace. Experiments on massive-scale data show the fast variants significantly accelerates the training process (such as 64×) with only a minor sacrifice in performance, outperforming existing acceleration methods of Softmax in terms of both performance and efficiency.

Topics: AAAI

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HOW TO CITE:

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning Proceedings of the AAAI Conference on Artificial Intelligence (2020) 10957-10964.

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning AAAI 2020, 10957-10964.

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang (2020). Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 10957-10964.

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang. Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.10957-10964.

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang. 2020. Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning. "Proceedings of the AAAI Conference on Artificial Intelligence". 10957-10964.

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang. (2020) "Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning", Proceedings of the AAAI Conference on Artificial Intelligence, p.10957-10964

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang, "Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning", AAAI, p.10957-10964, 2020.

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang. "Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.10957-10964.

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang. "Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 10957-10964.

Lanqing He||Zhongdao Wang||Yali Li||Shengjin Wang. Softmax Dissection: Towards Understanding Intra- and Inter-Class Objective for Embedding Learning. AAAI[Internet]. 2020[cited 2023]; 10957-10964.


ISSN: 2374-3468


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