Semantic Bottleneck Networks add interpretable semantic concept layers to deep networks, recovering SOTA segmentation performance with drastic channel reduction and enabling failure interpretation at over 99% accuracy for most outputs.
Towards robust interpretability with self-explaining neural networks
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.CV 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Interpretability Beyond Classification Output: Semantic Bottleneck Networks
Semantic Bottleneck Networks add interpretable semantic concept layers to deep networks, recovering SOTA segmentation performance with drastic channel reduction and enabling failure interpretation at over 99% accuracy for most outputs.