What is human neural network?
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
How does DNN work?
A DNN is a collection of neurons organized in a sequence of multiple layers, where neurons receive as input the neuron activations from the previous layer, and perform a simple computation (e.g. a weighted sum of the input followed by a nonlinear activation).
How is a convolutional neural network an interpretable network?
This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable CNN, in order to clarify knowledge representations in high conv-layers of the CNN. In an interpretable CNN, each ・〕- ter in a high conv-layer represents a speci・… object part.
How are interpretable CNNs used in the learning process?
Our interpretable CNNs use the same training data as or- dinary CNNs without a need for any annotations of object parts or textures for supervision. The interpretable CNN automatically assigns each ・〕ter in a high conv-layer with an object part during the learning process.
Which is the Achilles heel of neural networks?
Besides the discrimina- tion power, model interpretability is another crucial prop- erty for neural networks. However, the interpretability is always an Achilles窶・heel of CNNs, and has presented chal- lenges for decades.
Why do we need an inter pretable CNN?
The explicit knowledge representation in an inter- pretable CNN can help people understand the logic inside a CNN,i.e.what patterns are memorized by the CNN for prediction. Experiments have shown that ・〕ters in an inter- pretable CNN are more semantically meaningful than those in a traditional CNN.