On automatical weight function learning for different training data bias cases:

Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, Deyu Meng*. Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting. NeurIPS, 2019.(Both sampling weighting schemes like easy sample emphasizing, e.g., self-paced learning, or hard sample emphasizing, e.g., focal loss, can be seen as special cases of this adaptive meta-learning framework.)

On blind image denoising by a variational denoising network:

Zongsheng Yue,Hongwei Yong, Qian Zhao, Lei Zhang, Deyu Meng*. Variational Denoising Network: Toward Blind Noise Modeling and Removal. NeurIPS, 2019. (Our method can learn an approximate posterior to the true posterior with the latent variables including clean image and noise distribution (non-i.i.d.) conditioned on the input noisy image. Using this variational posterior expression, both tasks of blind image denoising and noise estimation can be naturally attained in a unique Bayesian framework.)