LinearEvaluationProtocol Inlinear evaluation, wefollowthecommon setting [6,5]tofreeze the backbone of ResNet-50 and train a supervised linear classifier on the global average pooling features for100 epochs. Note that, the2-layer head inunsupervised pre-training isnotused inthe linear evaluation stage. During training, we augment the image with random scaling from 0.5 to 2.0, crop size of 769 and random flip. The top-1 and top-5 accuracyresults are reported inTable9. From the perspective of optimization goals, the only difference between the parametric instance classification framework and supervised classification framework is how to define the classes for each instance.

Body Problem: presenting distorted, incomplete, duplicated, or abnormal body parts (e.g., limbs)

We present an approach for lifelong/continual learning of convolutional neural networks (CNN) that does not suffer from the problem of catastrophic forgetting when moving from onetask totheother. Weshowthat theactivation maps generated by the CNN trained on the old task can be calibrated using very few calibration parameters, to become relevant to the new task.

Training a large-scale deep neural network in a large-scale dataset is challenging and time-consuming. The recent breakthrough of large-batch optimization is a promising way to tackle this challenge.