Researchers have evaluated nine class-incremental learning (CIL) methods on frozen protein encoders, finding that these methods do not reliably transfer across different protein representations. The study highlights that instability in CIL methods stems from weak gradient signals, varied embedding geometries, and covariance assumptions that change with dataset scale and encoder type. The findings suggest that future protein CIL approaches must be encoder-aware, adapting statistical assumptions and capacity to the specific geometry of the protein representation. AI
IMPACT Suggests a need for new methods in protein function annotation, potentially impacting drug discovery and biological research.
RANK_REASON Academic paper detailing a benchmark evaluation of existing methods on a new domain. [lever_c_demoted from research: ic=1 ai=1.0]
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