Visualization of ResNet18

About Data Set: 

I have been working on classification of a Kaggle plant seedling dataset with 12 classes, here are some manually picked examples from each class  :

Black Grass:

Charlock:

Cleavers:

Common Chickweed:

Common Wheat:

Fat Hen:

Loose Silky Bent:

Maize:

Scentless Mayweed:

Shepherd's purse:

Small Flowered Cranesbill:

Sugar Beet:

A ResNet18 pre-trained on imageNet have been fine tuned on this data set, achieving about 99% prediction accuracy.

Deep dream x ResNet18:

This week, I used deep dream to visualize each layer of the network, here is some result I find interesting:

Original image and Maximizing 'add layer' following stage2, 3, 4:

Spiral (maize seedling) and grey vertical line (bar code?) are encoded in stage 2; Star-like shape (intersect of thin leaves) and green color are encoded in stage 3; Line, curves and angles are encoded in stage 4;

Comparing with the result of mixed4c layer in Inception V3:

No higher level structure about plants emerged in any layer no matter how I change the parameters. (Probably due to the monotony of the dataset, no high level structure is necessary to classify the dataset?)

Input, 2 conv layers, output of Stage2 unit2:

 

Input, 2 conv layers, output of Stage3 unit2:

The output becomes the ''weighted mixture'' of mainstream and shortcut. (Can this explain the high performance of ResNet?)

Class Activation Map:

I also tried the class activation map of some randomly picked data samples, most of the sample have expected Heat map like this:

Several pictures have rather unexpected activation map:

For the sample on the left, the flower-like leaves should be a very good indication of clever class, but the network only looks at cotyledons. For the samples on the right, the network ignore the leaves on the center.