论文标题
部分可观测时空混沌系统的无模型预测
Dissecting the impact of different loss functions with gradient surgery
论文作者
论文摘要
PAIME损失是一种公制学习的方法，它通过优化鼓励来自同一语义类别的图像的损失功能来学习语义嵌入，比来自不同类别的图像更接近映射的图像。文献报道了成对损失策略的大量差异。在这里，我们将这些损耗函数的梯度分解为与它们如何推动锚阳性和锚定阴性对的相对特征位置有关的组件。这种分解允许统一大量当前成对损耗功能。此外，明确构建配对梯度更新以将这些效果分开，从而有最大影响的见解，并导致一种简单的算法，该算法击败了最终的最新算法，以在汽车，Cub和Stanford Online Products数据集上进行图像检索。
Pair-wise loss is an approach to metric learning that learns a semantic embedding by optimizing a loss function that encourages images from the same semantic class to be mapped closer than images from different classes. The literature reports a large and growing set of variations of the pair-wise loss strategies. Here we decompose the gradient of these loss functions into components that relate to how they push the relative feature positions of the anchor-positive and anchor-negative pairs. This decomposition allows the unification of a large collection of current pair-wise loss functions. Additionally, explicitly constructing pair-wise gradient updates to separate out these effects gives insights into which have the biggest impact, and leads to a simple algorithm that beats the state of the art for image retrieval on the CAR, CUB and Stanford Online products datasets.
