The code of implementation is available in https://github.com/UHIF/PID_AGD.

When such components are expected to be embedded in safety-critical systems (e.g.,

Unfortunately, very little to no attention is paid towards addressing calibration ofDNN-based visual object detectors, that occupysimilar space and importance inmanydecision making systems astheir visual classification counterparts. In this work, we study the calibration of DNN-based object detection models, particularly under domain shift.

Neural Information Processing Systems http://nips.cc/

We call this family of functions intra order-preserving functions. We propose a new neural network architecture that represents a class of intra order-preserving functions by combining common neural network components.

Both models consist of 2 layers and the hidden dimension is fixed to 64. We add a weight decay of 5e-4 for Cora, Citeseer, and Pubmed,and0fortherest. The optimizer configuration and the training schedule are the same as Section A.2. Kh(c ˆci) (7) where i N V denotes the evaluated node, andh is the bandwidth of the kernel function. The classwise-ECEs are summarized in Table 3, and the KDE-ECEs are collected in Table 4. Weadopt a heuristic which proportionally rescales the non top-1 output probabilities so that the calibrated probabilistic output sums up to one. While the ECEs ofCaGCN inits original paper are promising [23], we observethat the ECEs of CaGCN are often unstable and sometimes even worse than that of the uncalibrated model in our experiments.