论文标题
垫子：针对视觉相似性学习的政策调整的抽样
PADS: Policy-Adapted Sampling for Visual Similarity Learning
论文作者
论文摘要
学习视觉相似性需要学习关系，通常在图像的三胞胎之间。尽管三胞胎的方法很强大，但它们的计算复杂性主要将培训仅限于所有可能的训练三胞胎的子集。因此，对何时使用学习过程中何时使用哪些培训样本的取样策略至关重要。当前，突出的范式是固定的或在培训开始之前预定义的课程抽样策略。但是，这个问题确实需要进行抽样过程，该过程根据培训过程中相似性表示的实际状态进行调整。因此，我们采用强化学习，并让教师网络根据学习者网络的当前状态调整采样分布，该网络代表视觉相似性。使用基于标准的三重态损失的基准数据集进行实验表明，我们的自适应抽样策略显着优于固定采样策略。此外，尽管我们的自适应采样仅适用于基本三胞胎学习框架，但我们为采用多种额外的学习信号或强大的集合体系结构的最先进方法取得了竞争成果。可以在https://github.com/confusezius/cvpr2020_pads下找到代码。
Learning visual similarity requires to learn relations, typically between triplets of images. Albeit triplet approaches being powerful, their computational complexity mostly limits training to only a subset of all possible training triplets. Thus, sampling strategies that decide when to use which training sample during learning are crucial. Currently, the prominent paradigm are fixed or curriculum sampling strategies that are predefined before training starts. However, the problem truly calls for a sampling process that adjusts based on the actual state of the similarity representation during training. We, therefore, employ reinforcement learning and have a teacher network adjust the sampling distribution based on the current state of the learner network, which represents visual similarity. Experiments on benchmark datasets using standard triplet-based losses show that our adaptive sampling strategy significantly outperforms fixed sampling strategies. Moreover, although our adaptive sampling is only applied on top of basic triplet-learning frameworks, we reach competitive results to state-of-the-art approaches that employ diverse additional learning signals or strong ensemble architectures. Code can be found under https://github.com/Confusezius/CVPR2020_PADS.
