Self-paced　learning　(SPL)　is　a　recently　proposed　learning　regime　inspired　by　the　learning　process　of　humans　and　animals　that　gradually　incorporates　easy　to　more　complex　samples　into　training.　Existing　methods　are　limited　in　that　they　ignore　an　important　aspect　in　learning:　diversity.　To　incorporate　this　information,　we　propose　an　approach　called　self-paced　learning　with　diversity　(SPLD)　which　formalizes　the　preference　for　both　easy　and　diverse　samples　into　a　general　regularizer.　This　regularization　term　is　independent　of　the　learning　objective,　and　thus　can　be　easily　generalized　into　various　learning　tasks.　Albeit　non-convex,　the　optimization　of　the　variables　included　in　this　SPLD　regularization　term　for　sample　selection　can　be　globally　solved　in　linearithmic　time.　We　demonstrate　that　our　method　significantly　outperforms　the　conventional　SPL　on　three　real-world　datasets.　Specifically,　SPLD　achieves　the　best　MAP　so　far　reported　in　literature　on　the　Hollywood2　and　Olympic　Sports　datasets.