For tackling the task of 2D human pose estimation, the great majority of the recentmethods regardthistaskasaheatmap estimation problem, andoptimize the heatmap prediction using the Gaussian-smoothed heatmap as the optimization objective and using the pixel-wise loss (e.g.

Inthis experiment, we only quantize the weights, not the activations, to compare the performance degradation as weight bit-width decreases. The mean squared errors (MSE) of the weights across different bit-widths are also reported. The name of the layer and the number of parameters in parenthesis are shown in the column. All numbers are results of the last epoch. Table A3: ResNet-32 trained with Adam on the CIFAR-100 dataset.

Attackskm Success Untar 10 5 50 1 Targeted 10 5 50 1 Case Study:Westart stochasticdefense 2 [ 90,90] degrees (chosen tion.

However, overparameterization also leads to excessive computational and memory requirements.