Deep　neural　networks　require　correct　label　annotation　during　supervised　learning.　It　is　inevitable,　however,　that　some　labels　are　noisy　during　the　labeling　process.　A　deep　neural　network　retains　incorrect　labels　during　training,　resulting　in　a　degradation　of　performance.　Therefore,　it　is　essential　to　identify　samples　with　potentially　correct　labels.　In　state-of-the-art　methods,　small-loss　samples　are　chosen　for　subsequent　training　through　a　sample　selection　strategy.　Howerver,　it　typically　ignores　the　imbalance　in　noise　ratios　between　mini-batches　when　performing　sample　selection　within　each　minibatch.　Further,　numerous　valuable　samples　with　high　losses　are　discarded,　which　adversely　affects　the　generalization　performance　of　the　model,　particularly　under　conditions　of　high　noise　ratios.　To　this　end,　this　paper　proposes　IdentifyMix,　an　effective　two-stage　learning　approach　for　noisy　robust　learning　that　combines　an　unique　sample　selection　strategy　and　the　semi-supervised　learning　technique.　By　observing　how　the　dynamics　of　network　training　are　changing,　AUM　(Area　Under　the　Margin)　provides　a　criterion　that　is　applied　in　this　research　to　identify　mislabeled　data.　Moreover,　by　combining　semi-supervised　learning　with　contrastive　learning　and　data　augmentation,　it　is　possible　to　extract　more　useful　information　from　mislabeled　samples.　Experiments　on　several　synthetic　and　real-world　noise　benchmarks　demonstrate　the　effectiveness　of　IdentifyMix　compared　with　state-of-the-art　methods.　　©　2023　Journal　of　Applied　and　Numerical　Optimization.