Equivariant representation learning aims to capture variations induced by input transformations in the representation space, whereas invariant representation learning encodes semantic information by disregarding such transformations. Recent studies have shown that jointly learning both types of representations is often beneficial for downstream tasks, typically by employing separate projection heads. However, this design overlooks information shared between invariant and equivariant learning, which leads to redundant feature learning and inefficient use of model capacity. To address this, we introduce Soft Task-Aware Routing (STAR), a routing strategy for projection heads that models them as experts. STAR induces the experts to specialize in capturing either shared or task-specific information, thereby reducing redundant feature learning. We validate this effect by observing lower canonical correlations between invariant and equivariant embeddings. Experimental results show consistent improvements across diverse transfer learning tasks. The code is available at https://github.com/YonseiML/star.

翻译：等变表示学习旨在捕捉输入变换在表示空间中引发的变异，而不变表示学习则通过忽略此类变换来编码语义信息。近期研究表明，通过使用独立的投影头联合学习这两种表示通常对下游任务有益。然而，这种设计忽略了不变学习与等变学习之间共享的信息，导致冗余特征学习及模型容量利用效率低下。为解决此问题，我们提出了软任务感知路由（STAR），一种将投影头建模为专家的路由策略。STAR促使专家专注于捕捉共享信息或任务特定信息，从而减少冗余特征学习。我们通过观察不变嵌入与等变嵌入间较低的典型相关性验证了这一效果。实验结果表明，该方法在多种迁移学习任务中均取得了一致性提升。代码发布于 https://github.com/YonseiML/star。