Recently,　Generative　Adversarial　Network　(GAN)　has　been　adopted　to　improve　person　re-identification　(person　re-ID)　performance　through　data　augmentation.　However,　directly　leveraging　generated　data　to　train　a　re-ID　model　may　easily　lead　to　over-fitting　issue　on　these　extra　data　and　decrease　the　generalisability　of　model　to　learn　true　ID-related　features　from　real　data.　Inspired　by　the　previous　approach　which　assigns　multi-pseudo　labels　on　the　generated　data　to　reduce　the　risk　of　over-fitting,　we　propose　to　take　sparse　regularization　into　consideration.　We　attempt　to　further　improve　the　performance　of　current　re-ID　models　by　using　the　unlabeled　generated　data.　The　proposed　Sparse　Regularized　Multi-Pseudo　Label　(SRMpL)　can　effectively　prevent　the　over-fitting　issue　when　some　larger　weights　are　assigned　to　the　generated　data.　Our　experiments　are　carried　out　on　two　publicly　available　person　re-ID　datasets　(e.g.,　Market-1501　and　DukeMTMC-reID).　Compared　with　existing　unlabeled　generated　data　re-ID　solutions,　our　approach　achieves　competitive　performance.　Two　classical　re-ID　models　are　used　to　verify　our　sparse　regularization　label　on　generated　data,　i.e.,　an　ID-embedding　network　and　a　two-stream　network.