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The Inception v1 architecture is a deep CNN composed of 22 layers. Most of these layers were "Inception modules". The original paper stated that Inception modules are a "logical culmination" of Network in Network [5] and (Arora et al, 2014). [6] Since Inception v1 is deep, it suffered from the vanishing gradient problem.
It has been used to measure the quality of many recent models including the high-resolution StyleGAN1 [4] and StyleGAN2 [5] networks, and diffusion models. [2] [3] The FID attempts to compare images visually through deep layers of an inception network. More recent works take this further by instead comparing CLIP embeddings of the images. [6] [7]
The Inception Score (IS) is an algorithm used to assess the quality of images created by a generative image model such as a generative adversarial network (GAN). [1] The score is calculated based on the output of a separate, pretrained Inception v3 image classification model applied to a sample of (typically around 30,000) images generated by the generative model.
AlexNet is highly influential, resulting in much subsequent work in using CNNs for computer vision and using GPUs to accelerate deep learning. As of mid 2024, the AlexNet paper has been cited over 157,000 times according to Google Scholar.
During the early days of deep learning, there were attempts to train increasingly deep models. Notable examples included the AlexNet (2012), which had 8 layers, and the VGG-19 (2014), which had 19 layers. [24] However, stacking too many layers led to a steep reduction in training accuracy, [25] known as the "degradation" problem. [1]
The groom disagreed with his wife, countering that his friend was "just joking." "But I don’t find anything funny about that," the bride insisted.
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A deep Q-network (DQN) is a type of deep learning model that combines a deep neural network with Q-learning, a form of reinforcement learning. Unlike earlier reinforcement learning agents, DQNs that utilize CNNs can learn directly from high-dimensional sensory inputs via reinforcement learning. [154]