Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search

Learning from mistakes is an effective learning approach widely used in human learning, where a learner pays greater focus on mistakes to circumvent them in the future. It aids in improving the overall learning outcomes. In this work, we aim to investigate how effectively this exceptional learning ability can be used to improve machine learning models as well. We propose a simple and effective multi-level optimization framework called learning from mistakes (LFM), inspired by mistake-driven learning to train better machine learning models. Our LFM framework consists of a formulation involving three learning stages. The primary objective is to train a model to perform effectively on target tasks by using a re-weighting technique to prevent similar mistakes in the future. In this formulation, we learn the class weights by minimizing the validation loss of the model and re-train the model with the synthetic data from the image generator weighted by class-wise performance and real data. We apply our LFM framework for differential architecture search methods on image classification datasets such as CIFAR and ImageNet, where the results demonstrate the effectiveness of our proposed strategy.