for a total of 100K iterations, with a step size of 30K. As baseline, we also report numbers for CNN learned using Flickr images alone (FlickrS) and combined Google+Flickr images (GFAll). Note in case of GFAll, neither two stage learning or relationship graph constraint is used.

**Is Confusion Matrix Informative for Relationships?** We first want to show if the network has learned to discover the look-alike relationships between concepts in the confusion matrix. To verify the quality of the network, we take the GoogleO net and visualize the top-5 most confusing concepts (including self) to some of the categories. To ensure our selection has a good coverage, we first rank the diagonal of the confusing matrix (accuracy) in the descending order. Then we randomly sample 3 categories from the top-100, bottom-100, and middle-100 from the list. The visualization and explanations can be found in Figure \[fig:rel\]. We can see that the top relationships learned are indeed reasonable.

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![image](subcat){width="0.97\linewidth"}

PASCAL VOC Object Detection\[sec:det\]
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Next, we test our webly trained CNN model for object detection on the PASCAL VOC. Following the R-CNN pipeline, two sets of experiments are performed on VOC 2007. First, we directly test the generalizability of CNN-representations 