论文标题
通过深层嵌入学习代理
Learning Surrogates via Deep Embedding
论文作者
论文摘要
本文提出了一种通过最大程度地减少替代目标评估度量的替代损失来训练神经网络的技术，这可能是不可差异的。替代物是通过深层嵌入来学到的，其中预测与地面真理之间的欧几里得距离对应于评估度量的值。在调节后设置中证明了所提出的技术的有效性，其中使用学识渊博的替代物调整了训练有素的模型。没有大量的计算开销和任何铃铛和哨子，就可以在场景文本识别和检测的具有挑战性和实际任务上进行改进。在识别任务中，该模型使用近似于编辑距离度量的替代物调整，并在总编辑距离中实现高达$ 39 \％$的相对改进。在检测任务中，替代物在旋转边界框上近似于Union Metric的交叉点，并且在$ f_ {1} $得分中的相对改善均高达$ 4.25 \％$。
This paper proposes a technique for training a neural network by minimizing a surrogate loss that approximates the target evaluation metric, which may be non-differentiable. The surrogate is learned via a deep embedding where the Euclidean distance between the prediction and the ground truth corresponds to the value of the evaluation metric. The effectiveness of the proposed technique is demonstrated in a post-tuning setup, where a trained model is tuned using the learned surrogate. Without a significant computational overhead and any bells and whistles, improvements are demonstrated on challenging and practical tasks of scene-text recognition and detection. In the recognition task, the model is tuned using a surrogate approximating the edit distance metric and achieves up to $39\%$ relative improvement in the total edit distance. In the detection task, the surrogate approximates the intersection over union metric for rotated bounding boxes and yields up to $4.25\%$ relative improvement in the $F_{1}$ score.
