text: 4,55,1384396211248013312,56468788,Ehsan Adeli,"Check our paper @CVPR 2021: Metadata Normalization (MDN), a new batch-level operation (end2end training) to correct the influence of metadata (#bias, #confounder, you name it) on feature distributions. W/ @drfeifei @jcniebles et al. <LINK> <LINK> <LINK> Code will be released soon: <LINK> Many thanks to the team from @StanfordAILab @StanfordSVL @StanfordMed, especially Mandy Lu and Qingyu Zhao, for this great work!",https://arxiv.org/abs/2104.09052,"Batch Normalization (BN) and its variants have delivered tremendous success in combating the covariate shift induced by the training step of deep learning methods. While these techniques normalize feature distributions by standardizing with batch statistics, they do not correct the influence on features from extraneous variables or multiple distributions. Such extra variables, referred to as metadata here, may create bias or confounding effects (e.g., race when classifying gender from face images). We introduce the Metadata Normalization (MDN) layer, a new batch-level operation which can be used end-to-end within the training framework, to correct the influence of metadata on feature distributions. MDN adopts a regression analysis technique traditionally used for preprocessing to remove (regress out) the metadata effects on model features during training. We utilize a metric based on distance correlation to quantify the distribution bias from the metadata and demonstrate that our method successfully removes metadata effects on four diverse settings: one synthetic, one 2D image, one video, and one 3D medical image dataset. ",Metadata Normalization,2,"['Check our paper @CVPR 2021: Metadata Normalization (MDN), a new batch-level operation (end2end training) to correct the influence of metadata (#bias, #confounder, you name it) on feature distributions. W/ @drfeifei @jcniebles et al.\n<LINK>\n<LINK> <LINK>', 'Code will be released soon: https://t.co/BSvhIFzbBg\nMany thanks to the team from @StanfordAILab @StanfordSVL @StanfordMed, especially Mandy Lu and Qingyu Zhao, for this great work!']",21,04,417