AlexNet
AlexNet is a convolutional neural network (CNN) architecture that marked a significant breakthrough in the field of Deep Learning and Computer Vision. Here's a detailed overview:
History and Context
Architecture Details
AlexNet consists of:
- Eight Layers: Five convolutional layers, some followed by Max Pooling layers, and three fully connected layers.
- ReLU Non-linearities: Utilizes the Rectified Linear Unit (ReLU) activation function which was shown to train much faster than the previously common tanh or sigmoid functions.
- Dropout: To reduce overfitting, dropout with a rate of 50% was applied to the first two fully connected layers.
- Data Augmentation: Techniques like random cropping, horizontal flipping, and color shifting were used to artificially enlarge the dataset.
- Normalization: Local Response Normalization was used after applying ReLU in some layers to enhance generalization.
- Parallelization: The model was designed to take advantage of multiple GPUs for training, splitting the network into two parts running on separate GPUs.
Impact and Influence
- AlexNet demonstrated that deep neural networks could achieve impressive results on complex visual recognition tasks if given enough data and computational power.
- It led to the development of subsequent architectures like VGGNet, GoogLeNet, and ResNet, which further improved performance by going deeper and introducing novel architectures.
- Its success influenced the field to move towards larger datasets, more computational resources, and the development of specialized hardware like TPUs.
Sources
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