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The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality.
Model compression
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Multiple 3d object tracking for augmented reality
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Vision meets robotics: The kitti dataset
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Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2014
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Sparse 3d convolutional neural networks
Graham, B · 2015
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Fitnets: Hints for thin deep nets
Romero, A., Ballas, N., Kahou, S. E., Chassang, A., Gatta, C., and Bengio, Y · 2015
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Cross modal distillation for supervision transfer
Gupta, S., Hoffman, J., and Malik, J · 2016
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Han, S., Mao, H., and Dally, W. J · 2016
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Temporal ensembling for semi-supervised learning
Laine, S. and Aila, T · 2017
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Mimicking very efficient network for object detection
Li, Q., Jin, S., and Yan, J · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Tarvainen, A. and Valpola, H · 2017
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
Zagoruyko, S. and Komodakis, N · 2017
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PACT: parameterized clipping activation for quantized neural networks
Choi, J., Wang, Z., Venkataramani, S., Chuang, P. I., Srinivasan, V., and Gopalakrishnan, K · 2018
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Learning sparse neural networks through l_0 regularization
Louizos, C., Welling, M., and Kingma, D. P · 2018
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Shufflenet v2: Practical guidelines for efficient cnn architecture design
Ma, N., Zhang, X., Zheng, H.-T., and Sun, J · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
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Second: Sparsely embedded convolutional detection
Yan, Y., Mao, Y., and Li, B · 2018
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Voxelnet: End-to-end learning for point cloud based 3d object detection
Zhou, Y. and Tuzel, O · 2018
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Fast point r-cnn
Chen, Y., Liu, S., Shen, X., and Jia, J · 2019
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A comprehensive overhaul of feature distillation
Heo, B., Kim, J., Yun, S., Park, H., Kwak, N., and Choi, J. Y · 2019
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Searching for mobilenetv3
Howard, A., Pang, R., Adam, H., Le, Q. V., Sandler, M., Chen, B., Wang, W., Chen, L., Tan, M., Chu, G., Vasudevan, V., and Zhu, Y · 2019
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Pointpillars: Fast encoders for object detection from point clouds
Lang, A. H., Vora, S., Caesar, H., Zhou, L., Yang, J., and Beijbom, O · 2019
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Data-free quantization through weight equalization and bias correction
Nagel, M., Baalen, M. v., Blankevoort, T., and Welling, M · 2019
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Relational knowledge distillation
Park, W., Kim, D., Lu, Y., and Cho, M · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E. Z., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Contrastive representation distillation
Tian, Y., Krishnan, D., and Isola, P · 2020
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Bert-of-theseus: Compressing BERT by progressive module replacing
Xu, C., Zhou, W., Ge, T., Wei, F., and Zhou, M · 2020
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Task-oriented feature distillation
Zhang, L., Shi, Y., Shi, Z., Ma, K., and Bao, C · 2020
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General instance distillation for object detection
Dai, X., Jiang, Z., Wu, Z., Bao, Y., Wang, Z., Liu, S., and Zhou, E · 2021
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Teachers do more than teach: Compressing image-to-image models
Jin, Q., Ren, J., Woodford, O. J., Wang, J., Yuan, G., Wang, Y., and Tulyakov, S · 2021
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Instance-conditional knowledge distillation for object detection
Kang, Z., Zhang, P., Zhang, X., Sun, J., and Zheng, N · 2021
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Correlation congruence for knowledge distillation
Peng, B., Jin, X., Liu, J., Li, D., Wu, Y., Liu, Y., Zhou, S., and Zhang, Z · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T · 2019
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Pointrcnn: 3d object proposal generation and detection from point cloud
Shi, S., Wang, X., and Li, H · 2019
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3d point cloud generative adversarial network based on tree structured graph convolutions
Shu, D. W., Park, S. W., and Kwon, J · 2019
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Similarity-preserving knowledge distillation
Tung, F. and Mori, G · 2019
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Pointconv: Deep convolutional networks on 3d point clouds
Wu, W., Qi, Z., and Li, F · 2019
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Rethinking network design and local geometry in point cloud: A simple residual mlp framework
Ma, X., Qin, C., You, H., Ran, H., and Fu, Y · 2021
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Frustum-pointpillars: A multi-stage approach for 3d object detection using rgb camera and lidar
Paigwar, A., Sierra-Gonzalez, D., Erkent, Ö., and Laugier, C · 2021
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Offboard 3d object detection from point cloud sequences
Qi, C. R., Zhou, Y., Najibi, M., Sun, P., Vo, K., Deng, B., and Anguelov, D · 2021
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Online multi-granularity distillation for GAN compression
Ren, Y., Wu, J., Xiao, X., and Yang, J · 2021
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Improve object detection with feature-based knowledge distillation: Towards accurate and efficient detectors
Zhang, L. and Ma, K · 2021
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SE-SSD: self-ensembling single-stage object detector from point cloud
Zheng, W., Tang, W., Jiang, L., and Fu, C · 2021
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Adaptive graph convolution for point cloud analysis
Zhou, H., Feng, Y., Fang, M., Wei, M., Qin, J., and Lu, T · 2021
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Monodistill: Learning spatial features for monocular 3d object detection
Chong, Z., Ma, X., Zhang, H., Yue, Y., Li, H., Wang, Z., and Ouyang, W · 2022
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Finding the task-optimal low-bit sub-distribution in deep neural networks
Dong, R., Tan, Z., Wu, M., Zhang, L., and Ma, K · 2022
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Embracing single stride 3d object detector with sparse transformer
Fan, L., Pang, Z., Zhang, T., Wang, Y., Zhao, H., Wang, F., Wang, N., and Zhang, Z · 2022
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Image-to-lidar self-supervised distillation for autonomous driving data
Sautier, C., Puy, G., Gidaris, S., Boulch, A., Bursuc, A., and Marlet, R · 2022
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Wavelet knowledge distillation: Towards efficient image-to-image translation
Zhang, L., Chen, X., Tu, X., Wan, P., Xu, N., and Ma, K · 2022
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