论文标题
学习通过深度互动重新享用
Learning to Reweight with Deep Interactions
论文作者
论文摘要
最近，将教学概念引入了机器学习中，其中使用教师模型来指导学生模型（将用于实际任务中）通过数据选择，损失功能设计等进行。在现有的学习重量工作时，教师模型仅利用较浅/表面信息，例如培训/验证集中的学生模型培训迭代编号以及损失/准确性，但忽略了学生模型的内部状态，这限制了学习重新持续的潜力。在这项工作中，我们提出了一种改进的数据重新加权算法，其中学生模型为教师模型提供了内部状态，而教师模型则返回培训样本的自适应权重，以增强学生模型的培训。使用验证集繁殖的元梯度共同培训教师模型。具有清洁/嘈杂标签和神经机器翻译的图像分类实验在经验上表明，我们的算法比以前的方法显着改善。
Recently, the concept of teaching has been introduced into machine learning, in which a teacher model is used to guide the training of a student model (which will be used in real tasks) through data selection, loss function design, etc. Learning to reweight, which is a specific kind of teaching that reweights training data using a teacher model, receives much attention due to its simplicity and effectiveness. In existing learning to reweight works, the teacher model only utilizes shallow/surface information such as training iteration number and loss/accuracy of the student model from training/validation sets, but ignores the internal states of the student model, which limits the potential of learning to reweight. In this work, we propose an improved data reweighting algorithm, in which the student model provides its internal states to the teacher model, and the teacher model returns adaptive weights of training samples to enhance the training of the student model. The teacher model is jointly trained with the student model using meta gradients propagated from a validation set. Experiments on image classification with clean/noisy labels and neural machine translation empirically demonstrate that our algorithm makes significant improvement over previous methods.
