In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

We propose an approach to training machine learning models that are fair in the sense that their performance is invariant under certain perturbations to the features. For example, the performance of a resume screening system should be invariant under changes to the name of the applicant. We formalize this intuitive notion of fairness by connecting it to the original notion of individual fairness put forth by Dwork et al and show that the proposed approach achieves this notion of fairness. We also demonstrate the effectiveness of the approach on two machine learning tasks that are susceptible to gender and racial biases.
Algorithm for training individually fair classifier using adversarial robustness

Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with 100% accuracy. In this work we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets easier with increasing manifold complexity, and present a theoretical understanding of this behavior. Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions.
We investigate ReLU networks in the Fourier domain and demonstrate peculiar behaviour.

A well-trained model should classify objects with unanimous score for every category. This requires the high-level semantic features should be alike among samples, despite a wide span in resolution, texture, deformation, etc. Previous works focus on re-designing the loss function or proposing new regularization constraints on the loss. In this paper, we address this problem via a new perspective. For each category, it is assumed that there are two sets in the feature space: one with more reliable information and the other with less reliable source. We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher's behavior and thus pushing towards a more compact class centroid in the high-dimensional space. Such a scheme also benefits the reliable set since samples become more closer within the same category - implying that it is easilier for the classifier to identify. We refer to this mutual learning process as feature intertwiner and embed the spirit into object detection. It is well-known that objects of low resolution are more difficult to detect due to the loss of detailed information during network forward pass. We thus regard objects of high resolution as the reliable set and objects of low resolution as the less reliable set. Specifically, an intertwiner is achieved by minimizing the distribution divergence between two sets. We design a historical buffer to represent all previous samples in the reliable set and utilize them to guide the feature learning of the less reliable set. The design of obtaining an effective feature representation for the reliable set is further investigated, where we introduce the optimal transport (OT) algorithm into the framework. Samples in the less reliable set are better aligned with the reliable set with aid of OT metric. Incorporated with such a plug-and-play intertwiner, we achieve an evident improvement over previous state-of-the-arts on the COCO object detection benchmark.
(Camera-ready version) A feature intertwiner module to leverage features from one accurate set to help the learning of another less reliable set.