aeb7b30ef1d024a76f21a1d40e30c302-Supplemental.pdf

In D, we show the proofs of the two propositions formulated in the main text. We further provide the results of evaluating our models using various metrics other than ECE (like AdaECE, Classwise-ECE, MCE and NLL). To empirically observe this, we use the ResNet-50 network used for the analysis in 3. We divide In Figure A.1, we also show Here we show why focal loss favours accurate but relatively less confident solutions. Experimentally, we found the solution of the cross-entropy and focal loss equations, i.e. the value of the predicted We consider a binary classification problem. Figures C.2 (b) and (c) show that running gradient descent with cross-entropy (CE) and focal loss (FL) both gives the same decision regions i.e. the weight vector Figure C.2: (a): Confidence of mis-classifications (b): Decision boundary of linear classifier trained Here we provide the proofs of both the propositions presented in the main text.