Locating　diverse　body　parts　and　perceiving　part　visibility　are　essential　to　person　re-identification　(re-ID).　Most　existing　methods　employ　an　extra　model,　e.g.,　pose　estimation　or　human　parsing,　to　locate　parts,　or　generate　pseudo　labels　to　train　the　part　locator　incorporated　with　the　re-ID　model.　In　this　paper,　we　aim　at　learning　diverse　horizontal　stripes　with　foreground　refinement　to　pursue　pixel-level　part　alignment　via　only　using　person　identity　labels.　Specifically,　we　proposed　a　Gumbel-Softmax　based　Differential　Categorical　Region　(DCR)　learning　method　and　make　two　contributions.　(1)　A　stripe-wise　regularization.　Given　an　image,　the　part　locator　produce　part　probability　maps.　The　continuous　values　in　the　probability　maps　are　discretized　into　zero　or　arg　max　value　in　the　horizontal　stripes　by　the　Gumbel-Softmax.　Gumbel-Softmax　allows　us　to　use　the　arg　max　discrete　value　for　part　diversity　regularization　in　the　forward　pass,　but　can　still　estimate　gradients　in　the　backward　pass.　(2)　A　self-refinement　method　to　suppress　the　background　noise　in　the　stripes.　We　employ　a　lightweight　foreground　perception　head　to　produce　foreground　probability　map　with　only　person　identity　labels　supervision.　Benefits　from　discretization　of　the　categorical　stripes,　we　can　conveniently　obtain　the　part　pseudo　label　by　element-wise　multiplying　the　categorical　stripes　with　foreground　probability　map.　Finally,　DCR　can　locate　the　body　parts　at　pixel-level　and　extract　part-aligned　representation.　Experimental　results　on　both　holistic　and　occluded　re-ID　datasets　confirm　that　our　approach　significantly　improves　the　learned　representation　and　the　achieved　performance　is　on　par　with　the　state-of-the-art　methods.　The　code　is　available　at　https://github.com/deepalchemist/differentiable-categorical-region