Network In Network
Abstract: We propose a novel deep network structure called "Network In Network" (NIN) to enhance model discriminability for local patches within the receptive field. The conventional convolutional layer uses linear filters followed by a nonlinear activation function to scan the input. Instead, we build micro neural networks with more complex structures to abstract the data within the receptive field. We instantiate the micro neural network with a multilayer perceptron, which is a potent function approximator. The feature maps are obtained by sliding the micro networks over the input in a similar manner as CNN; they are then fed into the next layer. Deep NIN can be implemented by stacking mutiple of the above described structure. With enhanced local modeling via the micro network, we are able to utilize global average pooling over feature maps in the classification layer, which is easier to interpret and less prone to overfitting than traditional fully connected layers. We demonstrated the state-of-the-art classification performances with NIN on CIFAR-10 and CIFAR-100, and reasonable performances on SVHN and MNIST datasets.
Preview
Synopsis
Overview
- Keywords: Deep Learning, Convolutional Neural Networks, Micro Neural Networks, Global Average Pooling, Classification
- Objective: Introduce a novel deep network structure called "Network In Network" (NIN) to enhance model discriminability for local patches within the receptive field.
- Hypothesis: Replacing traditional linear convolutional layers with multilayer perceptrons (MLPs) will improve feature abstraction and classification performance.
- Innovation: The introduction of mlpconv layers that utilize MLPs for local feature extraction and the use of global average pooling to replace fully connected layers, enhancing interpretability and reducing overfitting.
Background
Preliminary Theories:
- Convolutional Neural Networks (CNNs): Traditional architecture using linear filters followed by nonlinear activation functions to extract features from images.
- Generalized Linear Models (GLMs): Assumes linear separability of data, which may not hold for complex datasets.
- Maxout Networks: A type of neural network that uses max pooling over affine feature maps, allowing for better approximation of convex functions.
- Multilayer Perceptrons (MLPs): A type of neural network that can approximate any continuous function, providing a more powerful alternative to GLMs.
Prior Research:
- 2012: Alex Krizhevsky et al. introduced deep CNNs that achieved significant performance improvements on ImageNet.
- 2013: The maxout network was proposed, demonstrating improved performance on various benchmarks by approximating convex functions.
- 2013: Dropout was introduced as a regularization technique to prevent overfitting in neural networks.
Methodology
Key Ideas:
- mlpconv Layers: Each mlpconv layer replaces traditional convolutional filters with a multilayer perceptron, allowing for more complex feature extraction.
- Global Average Pooling: Instead of fully connected layers, the model uses global average pooling to summarize feature maps, reducing overfitting and improving interpretability.
- Stacking Structure: The NIN architecture consists of multiple mlpconv layers followed by a global average pooling layer.
Experiments:
- Evaluated on benchmark datasets: CIFAR-10, CIFAR-100, SVHN, and MNIST.
- Utilized dropout as a regularizer between mlpconv layers.
- Metrics included classification error rates on test datasets.
Implications: The design of NIN allows for better abstraction of features and reduces the risk of overfitting through global average pooling, which serves as a structural regularizer.
Findings
Outcomes:
- Achieved state-of-the-art performance on CIFAR-10 with a test error rate of 8.81% after data augmentation.
- For CIFAR-100, the model reached a test error of 35.68%, surpassing previous methods.
- On the SVHN dataset, the error rate was reduced to 2.35%, demonstrating the effectiveness of the NIN architecture.
Significance: NIN outperformed traditional CNNs and maxout networks, showcasing the advantages of using MLPs for local feature extraction and global average pooling for classification.
Future Work: Exploration of NIN for object detection tasks and further optimization of the architecture for various applications.
Potential Impact: Advancements in feature extraction techniques could lead to improved performance in a wide range of computer vision tasks, influencing future neural network designs.