Papers › Compressing 3DCNNs Based on Tensor Train Decomposition
Compressing 3DCNNs Based on Tensor Train Decomposition
Dingheng Wang, Guangshe Zhao, Guoqi Li, Lei Deng, Yang Wu
Three dimensional convolutional neural networks (3DCNNs) have been applied in many tasks, e.g., video and 3D point cloud recognition. However, due to the higher dimension of convolutional kernels, the space complexity of 3DCNNs is generally larger than that of traditional two dimensional convolutional neural networks (2DCNNs). To miniaturize 3DCNNs for the deployment in confining environments such as embedded devices, neural network compression is a promising approach. In this work, we adopt the tensor train (TT) decomposition, a straightforward and simple in situ training compression method, to shrink the 3DCNN models. Through proposing tensorizing 3D convolutional kernels in TT format, we investigate how to select appropriate TT ranks for achieving higher compression ratio. We have also discussed the redundancy of 3D convolutional kernels for compression, core significance and future directions of this work, as well as the theoretical computation complexity versus practical executing time of convolution in TT. In the light of multiple contrast experiments based on VIVA challenge, UCF11, and UCF101 datasets, we conclude that TT decomposition can compress 3DCNNs by around one hundred times without significant accuracy loss, which will enable its applications in extensive real world scenarios.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Hand Gesture Recognition | SHREC 2017 track on 3D Hand Gesture Recognition | 3DCNN_VIVA_4 | 14 gestures accuracy | 73121216 | #1 of 3 | Archive leaderboard | report |
| Hand-Gesture Recognition | VIVA Hand Gestures Dataset | Two 3DCNNs: LRN + HRN [11] | Accuracy | 77.5 | #1 of 4 | Archive leaderboard | report |
| Hand-Gesture Recognition | VIVA Hand Gestures Dataset | Accuracy | 6.86 | #2 of 4 | Archive leaderboard | report | |
| Hand-Gesture Recognition | VIVA Hand Gestures Dataset | 3DCNN_VIVA_1 | Accuracy-CN | 2303240 | #3 of 4 | Archive leaderboard | report |
| Hand-Gesture Recognition | VIVA Hand Gestures Dataset | 3DCNN_VIVA_2 | Accuracy-CN | -13585591 | #4 of 4 | Archive leaderboard | report |
| Quantization | CIFAR-10 | 3DCNN_VIVA_3 | MAP | 160327.04 | #1 of 2 | Archive leaderboard | report |
| Quantization | Knowledge-based: | 3DCNN_VIVA_5 | All | 84809664 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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