This paper introduces visualization techniques which gives insight into the function of intermediate feature layer and helps us to understand the performance contributions from various model layers. This will further enable us to observe the evolution of features during training and to diagnose problems with the model.
Before this paper was published, visualizations were limited to the 1st layer's filters. This was because we were taking a direct convolution between the weights of the first layer and the input pixels, hence those filters could be projected to the pixel space. But this interpretation was not applicable to the weights of the hidden layers. Below are the filters learned by the first layer of AlexNet (Why do visualizing these filters give what they are looking for? This comes from the fact that when a filter is made to convolve with a piece of data, the output is maximum when that piece of data is the filter itself). If this is done to the hidden layers, the results are not satisfying or interpretable.
What we need is a way to map these not so interpretable filters back to the input pixel space. This is done using a Deconvolutional Network (deconvnet). To find which part of the image activates a given convnet activation, we set all the other activations in the layer to 0 and pass the feature maps (activations) as input to the attached deconvnet.
Deconvolutional Network: They were proposed as a way of performing unsupervised learning. Here they are just used to examine an already learned result.
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Unpooling: Pooling operation is irreversible; hence they try to generate an approximate result by keeping track of locations of maxima (for MaxPooling) within each pooling region in a set of 'switch' variables. Hence while unpooling, the structure of stimulus is preserved.
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Rectification: Rectification is done using the ReLU non-linearity to ensure that the feature reconstructions are valid (positive).
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Filtering: The convolution operation of the convnet is reversed by using the transposed version (flipping each filter vertically and horizontally) of the learned filters and applying them to the reconstructed signal from above.
These steps are repeated until we get to the input layer. The reconstructions obtained from a single activation resembles a small piece of the input image weighted according to their contribution towards the feature activation.
For each layer, they select the top 9 strongest activations and project each to the pixel space. These projections reveal the different structures that excite a given feature map. Alongside these projections, the original image patches are shown too. Hierarchical nature of features can be seen.
Layer 1: edges. Layer 2: corners and colour conjunctions. Layer 3: captures similar textures. Layer 4: class-specific variation (dog faces, bird's legs). Layer 5: entire objects with significant pose variation (keyboards, dogs).
For a given feature map, they take the strongest activation, project it down to the pixel space (using the deconvnet approach) and study its evolution over various epochs (1, 2, 5, 10, 20, 30, 40, 64).
We see that the lower layer features develop and converge within a few epochs, whereas the higher layer develop only after a significant number of epochs.
Image augmentations (translation, rotation, scaling) have a dramatic effect in the first layer of the model, whereas a lesser impact is seen in the top feature layer. This is expected
They show that these projections of feature maps can help us improve a particular architecture. They project the feature maps of a pretrained Alex Net and study them to find 2 problems:
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First layer filters are a mix of extremely high and low frequency information, with little coverage of the mid frequencies.
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2nd layer visualization shows aliasing artifacts caused by the large stride 4 used in the 1st layer convolutions
To fix these problems, they make the following changes:
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Reduced the 1st layer filter size from 11x11 to 7x7
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Made the stride of the convolution 2 rather than 4
This improved the classification accuracy
Now we also want to confirm if our CNN is identifying the location of the object in the image. For this, they covered regions of the image with a grey box and performed the training. Below are the amazing results:
