We provide supplementary materials for the submission of Self-Weighted Contrastive Learning among Multiple Views for Mitigating Representation Degeneration. Specifically, Appendix A (Page1) shows all theoretical proofs and complexity analysis of SEM; Appendix B (Page-7) includes the settings in experiments; Appendix C (Page-8) lists additional experimental results and provides more experimental analysis, which are not shown in the paper due to space; Appendix D (Page-10) discusses the limitations and future work of this paper. The code implementation, trained models, and datasets used in our method are provided in https://github.com/SubmissionsIn/SEM. I(Xv;Hv), (8) where Wm,n > 0 as two views (v {m,n}) are with positive class mutual information. Therefore, if Hv is the tv-th layer's features (i.e., Hv(tv) act as the regularized hidden features), we have I(S;Zv) I(S;Xv) This design aims at separately maintaining different views' discriminative information by {Hv}Vv=1 and exploring their common semantic information by {Zv}Vv=1.

Furthermore, [30, 31] pointed out to conduct CL with reconstruction regularization to achieve robust representations for downstream tasks. RINCE [15] (a short name of Robust InfoNCE) is a variant of InfoNCE contrastive loss that considers noise in false positive sample pairs.