Cross-layer parameter sharing is a technique in neural network design where the same set of parameters, such as weights, are reused across multiple layers of the model. Instead of each layer having its own unique parameters, some or all layers share these values, which helps reduce the total number of parameters in the network. This…
Category: Deep Learning
Neural Network Ensemble Pruning
Neural network ensemble pruning is a technique used to make collections of neural networks more efficient. When many models are combined to improve prediction accuracy, the group can become slow and resource-intensive. Pruning involves removing some networks from the ensemble, keeping only those that contribute most to performance. This helps keep the benefits of using…
Squeeze-and-Excitation Modules
Squeeze-and-Excitation Modules are components added to neural networks to help them focus on the most important features in images or data. They work by learning which channels or parts of the data are most useful for a task, and then highlighting those parts while reducing the influence of less useful information. This process helps improve…
Neural Network Activation Functions
Neural network activation functions are mathematical formulas used in artificial neural networks to decide whether a neuron should be activated or not. They help the network learn complex patterns by introducing non-linearities, which means the network can handle more complicated tasks. Without activation functions, a neural network would only be able to solve simple problems…
Adaptive Dropout Methods
Adaptive dropout methods are techniques used in training neural networks to prevent overfitting by randomly turning off some neurons during each training cycle. Unlike standard dropout, adaptive dropout adjusts the dropout rate based on the importance or activity of each neuron, allowing the model to learn which parts of the network are most valuable for…
Neural Network Regularisation Techniques
Neural network regularisation techniques are methods used to prevent a model from becoming too closely fitted to its training data. When a neural network learns too many details from the examples it sees, it may not perform well on new, unseen data. Regularisation helps the model generalise better by discouraging it from relying too heavily…
Neural Architecture Transfer
Neural Architecture Transfer is a method where a machine learning model’s structure, or architecture, developed for one task is reused or adapted for a different but related task. Instead of designing a new neural network from scratch, researchers use proven architectures as a starting point and modify them as needed. This approach saves time and…
Neural Network Search Spaces
Neural network search spaces refer to the set of all possible neural network designs that can be considered for a specific task. This includes choices like the number of layers, types of layers, connections between layers, activation functions, and other design options. Exploring this space helps researchers and engineers find the most effective neural network…
Multi-Branch Neural Networks
Multi-branch neural networks are a type of artificial neural network where the architecture splits into two or more separate paths, or branches, at certain points. Each branch can process different pieces of information or apply different transformations before combining their outputs. This structure allows the network to learn multiple types of features or handle different…
Adaptive Residual Networks
Adaptive Residual Networks are a type of artificial neural network that builds on the concept of residual networks, or ResNets, by allowing the network to adjust how much information is passed forward at each layer. In traditional ResNets, shortcut connections let information skip layers, which helps with training deeper networks. Adaptive Residual Networks improve on…