Review — BagNet: Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet (Image Classification)

BagNet: Bag-of-Feature (BoF) Models Using ResNet, Better Interpretability


1. Deep Bag-of-features Model (BagNet)

Deep Bag-of-features Models (BagNets): Network Architecture & Performance
The BagNet architecture is almost equivalent to the ResNet-50 architectures

2. Experimental Results

The most important contribution is the explanation of the network.

Heatmaps showing the class evidence extracted from of each part of the image. The spatial sum over the evidence is the total class evidence
Most informative image patches for BagNets.

This visualisation yields many insights: For example, book jackets are identified mainly by the text on the cover, leading to confusion with other text on t-shirts or websites.

Images misclassified by BagNet-33 and VGG-16 with heatmaps for true and predicted label and the most predictive image patches. Class probability reported for BagNet-33 (left) and VGG (right)

The letters in the “miniskirt” image are very salient, thus leading to the “book jacket” prediction.



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