ResNet-RS: A Faster and More Efficient Architecture for Image Classification
ResNet-RS is a family of deep neural network architectures designed for image classification tasks. It is an extension of the popular ResNet architecture that gained fame for its ability to train extremely deep networks without suffering from the vanishing gradient problem. The main improvement of ResNet-RS is its scalability and faster training times, along with maintaining high accuracy rates compared to other state-of-the-art models.
Background
Achieving high accuracy on image classification tasks is a challenging problem in the field of deep learning. The ImageNet dataset is one of the most widely used datasets for benchmarking image classification models. ResNet, introduced in 2015, was a major breakthrough in this field as it was one of the first models that could train networks hundreds of layers deep without suffering from the vanishing gradient problem. ResNet accomplished this by introducing residual connections that allow for training deeper networks by shortening the distance between the input and output of each layer. Since then, multiple variations of the ResNet architecture have been introduced that differ in the number of layers and additional techniques used for boosting performance.
Improvements to ResNet Architecture
The ResNet-RS architecture introduces two scaling strategies. The first strategy is to scale the model depth in regimes where overfitting can occur. Overfitting happens when the model becomes too complex, and the training data is memorized instead of learning the underlying features needed for accurate predictions. When overfitting occurs, including more layers may not always result in better performance. The second scaling strategy is to increase image resolution more slowly than previously recommended. This results in more computationally efficient models that can be trained without the need for expensive hardware accelerators.
Additional Improvements
ResNet-RS further improves the architecture by using various techniques including:
Cosine Learning Rate Schedule
The cosine learning rate schedule is a technique that gradually reduces the learning rate for the model over the course of training. This can help stabilize the training process by not taking too large of a step in the optimization process, leading to better convergence rates.
Label Smoothing
This technique reduces the confidence on ground-truth labels which leads to more stable predictions. By reducing the model's reliance on the true label, it is more likely to generalize better to unseen data.
Stochastic Depth
Stochastic depth is the process of randomly dropping layers during training. This helps prevent overfitting and improves the generalization abilities of the model.
RandAugment
RandAugment is a data augmentation technique that helps improve the robustness of the model by randomly applying a set of image transformations during training. These augmentations help the model learn more robust and invariant features.
Decreased Weight Decay
Weight decay is a technique that helps prevent overfitting during training by adding a regularization term to the loss function of the model. ResNet-RS decreases the weight decay term, resulting in a smoother training process and better generalization to new data.
Squeeze-and-Excitation
Squeeze-and-Excitation is a network block designed to integrate attention mechanisms into the model. Attention mechanisms allow the model to focus on specific features of the input that are most important for accurate predictions. By integrating attention into the model, the ResNet-RS architecture is capable of learning more refined and meaningful features, leading to better performance.
ResNet-D Architecture
The ResNet-D architecture is a deeper version of the ResNet architecture that contains more layers. By including ResNet-D in the ResNet-RS family, the model can extract more intricate features from the input that can lead to better performance.
ResNet-RS is a significant improvement to the ResNet architecture that addresses some of its scalability issues. By introducing new scaling strategies and additional techniques, the ResNet-RS family achieves comparable accuracies to other state-of-the-art models while training faster and requiring less computational resources. This architecture has tremendous potential in improving image classification in various fields, including healthcare, self-driving cars, and facial recognition technology.
