Deep learning approaches have provided state-of-the-art performance in many
applications by relying on large and overparameterized neural networks.
However, such networks have been shown to be very brittle and are difficult to
deploy on resource-limited platforms. Model pruning, i.e., reducing the size of
the network, is a widely adopted strategy that can lead to a more robust and
compact model. Many heuristics exist for model pruning, but empirical studies
show that some heuristics improve performance whereas others can make models
more brittle or have other side effects. This work aims to shed light on how
different pruning methods alter the network's internal feature representation
and the corresponding impact on model performance. To facilitate a
comprehensive comparison and characterization of the high-dimensional model
feature space, we introduce a visual geometric analysis of feature
representations. We decomposed and evaluated a set of critical geometric
concepts from the common adopted classification loss, and used them to design a
visualization system to compare and highlight the impact of pruning on model
performance and feature representation. The proposed tool provides an
environment for in-depth comparison of pruning methods and a comprehensive
understanding of how model response to common data corruption. By leveraging
the proposed visualization, machine learning researchers can reveal the
similarities between pruning methods and redundant in robustness evaluation
benchmarks, obtain geometric insights about the differences between pruned
models that achieve superior robustness performance, and identify samples that
are robust or fragile to model pruning and common data corruption to model
pruning and data corruption but also obtain insights and explanations on how
some pruned models achieve superior robustness performance