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In this work we present a new framework for neural networks compression with fine-tuning, which we called Neural Network Compression Framework (NNCF).
Simplex-valued data appear throughout statistics and machine learning, for example in the context of transfer learning and compression of deep networks.
The success of deep neural networks in many real-world applications is leading to new challenges in building more efficient architectures.
Neural network compression methods have enabled deploying large models on emerging edge devices with little cost, by adapting already-trained models to the constraints of these devices.
Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks of video or 3D point cloud recognition.
Directly adapting these methods to the task of semantic segmentation only brings marginal improvements.
There has recently been an increasing desire to evaluate neural networks locally on computationally-limited devices in order to exploit their recent effectiveness for several applications; such effectiveness has nevertheless come together with a considerable increase in the size of modern neural networks, which constitute a major downside in several of the aforementioned computationally-limited settings.
A key parameter that all existing compression techniques are sensitive to is the compression ratio (e. g., pruning sparsity, quantization bitwidth) of each layer.