How to achieve such spatial adaptation in a principled manner has been an open problem in both traditional model-based and modern learning-based approaches toward SISR. In this paper, we propose a new adaptive weighted loss for SISR to train deep networks focusing on challenging situations such as textured and edge pixels with high uncertainty.

However, such a method easily leads to severe overfitting.

For the experiments described in Section 3.5 of the main paper, all methods which required a Bayesian These residuals are obtained by first training a separate classifier per each class, and then calculating the residual as the error between the predicted and ground truth class. Training Hyperparameters For each method, we used a batch size of 128 and a learning rate of 0.001. Each method was trained until convergence for 200 epochs. To validate that our "non-noisy" class conditioning approach is RBCC, and the class ordering implies that each class is predicted before its parent classes. Results are shown in Figure 1.

Recurrent Classifier Chains (RCCs), a recurrent neural network extension of ensemble-based classifier chains, are the state-of-the-art exact multi-label classification method for maximizing subset accuracy.