Unsupervised　visual　representation　learning　has　gained　significant　attention　in　the　computer　vision　community,　driven　by　recent　advancements　in　contrastive　learning.　Most　existing　contrastive　learning　frameworks　rely　on　instance　discrimination　as　a　pretext　task,　treating　each　instance　as　a　distinct　category.　However,　this　often　leads　to　intra-class　collision　in　a　large　latent　space,　compromising　the　quality　of　learned　representations.　To　address　this　issue,　we　propose　a　novel　contrastive　learning　method　that　utilizes　randomly　generated　supervision　signals.　Our　framework　incorporates　two　projection　heads:　one　handles　conventional　classification　tasks,　while　the　other　employs　a　random　algorithm　to　generate　fixed-length　vectors　representing　different　classes.　The　second　head　executes　a　supervised　contrastive　learning　task　based　on　these　vectors,　effectively　clustering　instances　of　the　same　class　and　increasing　the　separation　between　different　classes.　Our　method,　Contrastive　Learning　via　Randomly　Generated　Supervision(CLRGS),　significantly　improves　the　quality　of　feature　representations　across　various　datasets　and　achieves　state-of-the-art　performance　in　contrastive　learning　tasks.　©　2025　IEEE.