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A Unified Framework of Surrogate Loss by Refactoring and Interpolation

Author(s): Liu, Lanlan; Wang, Mingzhe; Deng, Jia

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Abstract: We introduce UniLoss, a unified framework to generate surrogate losses for training deep networks with gradient descent, reducing the amount of manual design of task-specific surrogate losses. Our key observation is that in many cases, evaluating a model with a performance metric on a batch of examples can be refactored into four steps: from input to real-valued scores, from scores to comparisons of pairs of scores, from comparisons to binary variables, and from binary variables to the final performance metric. Using this refactoring we generate differentiable approximations for each non-differentiable step through interpolation. Using UniLoss, we can optimize for different tasks and metrics using one unified framework, achieving comparable performance compared with task-specific losses. We validate the effectiveness of UniLoss on three tasks and four datasets. Code is available at
Publication Date: 2020
Citation: Liu, Lanlan, Mingzhe Wang, and Jia Deng. "A Unified Framework of Surrogate Loss by Refactoring and Interpolation." In European Conference on Computer Vision (2020): pp. 278-293. doi:10.1007/978-3-030-58580-8_17
DOI: 10.1007/978-3-030-58580-8_17
ISSN: 0302-9743
EISSN: 1611-3349
Pages: 278 - 293
Type of Material: Conference Article
Journal/Proceeding Title: European Conference on Computer Vision
Version: Author's manuscript

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