You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: Building deeper and larger convolutional neural networks (CNNs) is a primary trend for solving major visual recognition tasks [21, 9, 33, 5, 28, 24]. The most accurate CNNs usually have hundreds of layers and thousands of channels [9, 34, 32, 40], thus requiring computation at billions of FLOPs. This report examines the opposite extreme: pursuing the best accuracy in very limited computational budgets at tens or hundreds of MFLOPs, focusing on common mobile platforms such as drones, robots, and smartphones. Note that many existing works [16, 22, 43, 42, 38, 27] focus on pruning, compressing, or low-bit representing a “basic” network architecture. Here we aim to explore a highly efficient basic architecture specially designed for our desired computing ranges. This direction aims to accelerate inference while preserving accuracy of a pre-trained model.Pruning network connections [6, 7] or channels [38] reducesredundant connections in a pre-trained model while maintaining performance.Quantization [31, 27, 39, 45, 44] andfactorization [22, 16, 18, 37] are proposed inliterature to reduce redundancy in calculations to speed up inference.Without modifying the parameters, optimized convolution algorithms implemented by FFT [25, 35] and other methods [2] decrease time consumption in practice.Distilling [11] transfers knowledge fromlarge models into small ones, which makes training small models easier.

Question: What do you mean by model pruning?
Although model pruning is a specific concept in deep learning, it refers to reducing the model size by removing redundant network connections or channels.