What are the key improvements and features introduced in the first Inception model proposed in 'Going deeper with convolutions'?
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The Inception model also pioneered the idea of using auxiliary classifiers to aid training and improve gradients across the network.
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Another key improvement was the introduction of 'dimensionality reduction' to reduce the dimensionality within the network and prevent the 'curse of dimensionality'.
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The first Inception model introduced the concept of 'inception modules' which comprised of multiple convolutional layers with different filter sizes parallelly processing the input.
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It incorporated the use of 1x1 convolutions which helped reduce the computational complexity and increase model efficiency.
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