Improve Network Accuracy through Network Manipulation
- 1 min
Deep learning has been growing rapidly over the past couple decades due to its ability in solving extremely complex problems. However, this machine learning method is often considered as a “black box” since it is unclear how the neurons of a deep learning model work together to arrive at the final output. A recently found method called Network Dissection has solved this interpretability issue by coming up with a visual that shows what each neuron looks for and why. Results show that groups of neurons detect different human-interpretable concepts. Inspired by Network Dissection, we will be investigating methods to improve a convolutional neural network’s prediction power based on the knowledge of the role of each neuron. One such method is FocusedDropout: keeping neurons that focus on human-readable concepts while discarding everything else. Additionally, the effect of regularization of input gradients on model robustness and interpretability is also tested.