# DAVIDSTUTZ

Check out our latest research on adversarial robustness and generalization of deep networks.
21thMARCH2017

For object detection on KITTI, they use a fixed size bounding box for each category (e.g. pedestrian, vehicle, ciclyst etc.). For each category, a binary classifier is used — represented by comparably shallow 3D convolutional networks as illustrated in Figure 2. Each sparse convolutional layer is followed by rectified linear units in order to preserve sparsity. Furthermore, biases used in the convolutional layers are constrained to be negative. Training is done using the hinge loss, including weight decay and a $L_1$ regularizer for sparsity. The model is trained on an augmented set of positive and negative examples by randomly rotating and translating them. Every no and then, hard negatives are mined and added to the training set.