 This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a  greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .
<meaning-changed>  This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a  greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .
 This paper proposes contrastive conditional transport (CCT) that defines its CL loss over dependent sample-query pairs, which in practice is realized by drawing a random query, randomly selecting positive and negative samples, and contrastively reweighting these samples according to their distances to the query , exerting a doubly CL strategy that contrasts positive samples and negative ones within themselves separately. We realize this strategy with contrastive attraction and contrastive repulsion (CACR) makes the query not only exert a greater force to both pull more distant positive samples towards the query and push closer negative samples away from the query .