Which architecture is MOST likely to produce a model that detects whether a car is present in an image with the highest accuracy?
Use a deep convolutional neural network (CNN) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a linear output layer that outputs the probability that an image contains a car.
Use a deep multilayer perceptron (MLP) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.
Explanations:
While a deep CNN is suitable for image classification, a linear output layer is not appropriate for binary classification tasks, as it does not output probabilities that sum to 1. Instead, a sigmoid function should be used for binary outputs.
A deep CNN with a softmax output layer is ideal for multi-class classification. However, for binary classification (car vs. no car), it is typically preferred to use a sigmoid function, which can also be achieved using softmax with two output units. This architecture is capable of providing high accuracy for detecting cars in images.
A deep MLP is less effective than a CNN for image processing due to the lack of spatial hierarchies and features extraction. Furthermore, a linear output layer does not provide the necessary probabilistic interpretation for classification tasks.
Similar to option C, a deep MLP lacks the ability to effectively process image data compared to CNNs. Although a softmax output layer is appropriate for multi-class problems, a sigmoid function would be better suited for a binary classification task.
I compute that the answer is:
Use a deep convolutional neural network (CNN) classifier with the images as input. Include a softmax output layer that outputs the probability that an image contains a car.