” biases. Intuitively, in the image where a car is driving on a high way in a desert, the “good” bias denoting the common-sense context is the highway, and the “bad” bias accounting for the noisy context factor is the desert. We tackle this problem with a novel causal interventional training ( CIT ) approach, where we control the observed context in each object class. We offer theoretical justifications for CIT and validate it with extensive classification experiments on CIFAR-10, CIFAR-100 and ImageNet, e.g. , surpassing the standard deep neural networks ResNet-34 and ResNet-50, respectively, by 0.95% and 0.70% accuracies on the ImageNet. Our code is open-sourced on the GitHub https://github.com/qinwei-hfut/CIT .1a:T482,Curriculum Learning (CL) selects the training samples from the easy to difficult to boost the classification results. Most existing variates of CL measure the difficulty level of an example in an intuitive way, i.e., the loss value between the prediction and the ground truth. This way ignores the different distances to every class boundary, which is implied in the prediction vectors. In this paper, we propose a novel CL framework, named Balance Loss Curriculum Learning(BLCL), to reveal the comprehensive difficulty level of an example and improve the curriculum process based on the deep architectures. We follow the teacher-student learning module and train the teacher & student networks simultaneously. The predictions learned by the teacher network indicates the bounda

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