Problem
Research questions and friction points this paper is trying to address.
task separability
explainable AI
multi-level probing
TRIPROBE: Probing Task Separability Beyond Classification for XAI
🤖 AI Summary
TriProbe通过多层次探测框架解决任务可分离性诊断问题，采用输入、特征和分类器三级探针及最大费舍尔判别比率度量方法。
📝 Abstract
Modern evaluation of learning pipelines often reduces to downstream accuracy, leaving open the question of why tasks succeed or fail. TriProbe addresses this gap with a multi-level probing framework for explainable diagnosis of task separability. Rather than treating models as black boxes, TriProbe traces how separability evolves across inputs, learned features, and final classifiers. It decomposes multi-task problems into binary subtasks and applies three complementary probes: a Foundational Probe on input spaces, a Latent Probe on feature representations, and a Final Probe on classifier outputs. Using Maximum Fisher's Discriminant Ratio as a principled separability metric, TriProbe identifies bottlenecks and affected task pairs. Experiments on the Roshambo sEMG benchmark show how TriProbe reveals hidden breakdowns, guiding data collection, validation, and architecture design.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multi-level probing framework
Maximum Fisher's Discriminant Ratio
task separability
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