  Recent research underscores the pivotal role of the Out-of-Distribution (OOD)
feature representation field scale in determining the efficacy of models in OOD
detection. Consequently, the adoption of model ensembles has emerged as a
prominent strategy to augment this feature representation field, capitalizing
on anticipated model diversity.
  However, our introduction of novel qualitative and quantitative model
ensemble evaluation methods, specifically Loss Basin/Barrier Visualization and
the Self-Coupling Index, reveals a critical drawback in existing ensemble
methods. We find that these methods incorporate weights that are
affine-transformable, exhibiting limited variability and thus failing to
achieve the desired diversity in feature representation.
  To address this limitation, we elevate the dimensions of traditional model
ensembles, incorporating various factors such as different weight
initializations, data holdout, etc., into distinct supervision tasks. This
innovative approach, termed Multi-Comprehension (MC) Ensemble, leverages
diverse training tasks to generate distinct comprehensions of the data and
labels, thereby extending the feature representation field.
  Our experimental results demonstrate the superior performance of the MC
Ensemble strategy in OOD detection compared to both the naive Deep Ensemble
method and a standalone model of comparable size. This underscores the
effectiveness of our proposed approach in enhancing the model's capability to
detect instances outside its training distribution.
