2,726 research outputs found
Interpreting CNN Knowledge via an Explanatory Graph
This paper learns a graphical model, namely an explanatory graph, which
reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering
that each filter in a conv-layer of a pre-trained CNN usually represents a
mixture of object parts, we propose a simple yet efficient method to
automatically disentangles different part patterns from each filter, and
construct an explanatory graph. In the explanatory graph, each node represents
a part pattern, and each edge encodes co-activation relationships and spatial
relationships between patterns. More importantly, we learn the explanatory
graph for a pre-trained CNN in an unsupervised manner, i.e., without a need of
annotating object parts. Experiments show that each graph node consistently
represents the same object part through different images. We transfer part
patterns in the explanatory graph to the task of part localization, and our
method significantly outperforms other approaches.Comment: in AAAI 201
Examining CNN Representations with respect to Dataset Bias
Given a pre-trained CNN without any testing samples, this paper proposes a
simple yet effective method to diagnose feature representations of the CNN. We
aim to discover representation flaws caused by potential dataset bias. More
specifically, when the CNN is trained to estimate image attributes, we mine
latent relationships between representations of different attributes inside the
CNN. Then, we compare the mined attribute relationships with ground-truth
attribute relationships to discover the CNN's blind spots and failure modes due
to dataset bias. In fact, representation flaws caused by dataset bias cannot be
examined by conventional evaluation strategies based on testing images, because
testing images may also have a similar bias. Experiments have demonstrated the
effectiveness of our method.Comment: in AAAI 201
- …