Help Understanding Interpretability Techniques: Grad-CAM, Local Interpretable Model-Agnostic Explanations, and Activation Maximization

Hi everyone,

I've just trained a CNN that to determine if satellite images contain a coastline or not. It's pretty accurate, with an F1 of 90.09%

class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=10, kernel_size=3)
self.conv1_drop = nn.Dropout2d(p=0.5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=3)
self.conv2_drop = nn.Dropout2d(p=0.5)
self.fc1 = nn.Linear(18000, 1024)
self.fc2 = nn.Linear(1024, 2)

def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1_drop(self.conv1(x)), 2))
x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
x = x.view(x.shape[0], -1)
x = F.relu(self.fc1(x))
x = F.dropout(x, training=self.training)
x = …

cnn computervision grad-cam images interpretability model-agnostic satellite satellite images understanding