Convolutional　Neural　Networks　(CNNs)　have　delivered　impressive　state-of-the-art　performances　for　many　vision　tasks,　while　the　computation　costs　of　these　networks　during　test-time　are　notorious.　Empirical　results　have　discovered　that　CNNs　have　learned　the　redundant　representations　both　within　and　across　different　layers.　When　CNNs　are　applied　for　binary　classification,　we　investigate　a　method　to　exploit　this　redundancy　across　layers,　and　construct　a　cascade　of　classifiers　which　explicitly　balances　classification　accuracy　and　hierarchical　feature　extraction　costs.　Our　method　cost-sensitively　selects　feature　points　across　several　layers　from　trained　networks　and　embeds　non-expensive　yet　discriminative　features　into　a　cascade.　Experiments　on　binary　classification　demonstrate　that　our　framework　leads　to　drastic　test-time　improvements,　e.g.,　possible　47.2x　speedup　for　TRECVID　upper　body　detection,　2.82x　speedup　for　Pascal　VOC2007　People　detection,　3.72x　for　INRIA　Person　detection　with　less　than　0.5%　drop　in　accuracies　of　the　original　networks.