Abstract: A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.

Abstract: A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.

Abstract: Network firewalls operate based on rules that define how a firewall should handle traffic passing through the firewall. At their most basic, firewall rules may indicate that certain network traffic should be denied from passing through a network firewall or indicate that certain network traffic should be allowed to pass through the network firewall. Manners of handling network traffic beyond simply allowing or denying the network traffic may also be defined by the rules. For instance, a rule may indicate that certain network traffic should be routed to a specific system. Thus, if an administrator of a network firewall determines that certain network traffic should be handled in a certain way by a network firewall, the administrator need only implement a firewall rule defining how that network traffic should be handled in the network firewall.

Abstract: A computer vision (CV) training system, includes: a supervised learning system to estimate a supervision output from one or more input images according to a target CV application, and to determine a supervised loss according to the supervision output and a ground-truth of the supervision output; an unsupervised learning system to determine an unsupervised loss according to the supervision output and the one or more input images; a weakly supervised learning system to determine a weakly supervised loss according to the supervision output and a weak label corresponding to the one or more input images; and a joint optimizer to concurrently optimize the supervised loss, the unsupervised loss, and the weakly supervised loss.

Abstract: Network firewalls operate based on rules that define how a firewall should handle traffic passing through the firewall. At their most basic, firewall rules may indicate that certain network traffic should be denied from passing through a network firewall or indicate that certain network traffic should be allowed to pass through the network firewall. Manners of handling network traffic beyond simply allowing or denying the network traffic may also be defined by the rules. For instance, a rule may indicate that certain network traffic should be routed to a specific system. Thus, if an administrator of a network firewall determines that certain network traffic should be handled in a certain way by a network firewall, the administrator need only implement a firewall rule defining how that network traffic should be handled in the network firewall.