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On-device training is essential for user personalisation and privacy.
Natural gradient works efficiently in learning
Amari, S.-I · 1998
Earlier work this paper cites.
Automated Flower Classification over a Large Number of Classes
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Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
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Earlier work this paper cites.
A Survey on Transfer Learning
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Human-level Concept Learning through Probabilistic Program Induction
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Earlier work this paper cites.
Training Deep Nets with Sublinear Memory Cost, 2016
Chen, T., Xu, B., Zhang, C., and Guestrin, C · 2016
Earlier work this paper cites.
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
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Earlier work this paper cites.
Meta-SGD: Learning to Learn Quickly for Few-Shot Learning
Li, Z., Zhou, F., Chen, F., and Li, H · 2017
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
Quantizing Deep Convolutional Networks for Efficient Inference: A Whitepaper
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Earlier work this paper cites.
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Ma, N., Zhang, X., Zheng, H.-T., and Sun, J · 2018
Earlier work this paper cites.
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Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Earlier work this paper cites.
Few-Shot Learning with Graph Neural Networks
Satorras, V. G. and Estrach, J. B · 2018
Earlier work this paper cites.
FGVCx fungi classification challenge 2018
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Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H. S., and Hospedales, T. M · 2018
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Ling, N., Wang, K., He, Y., Xing, G., and Xie, D · 2021
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TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive?
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TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning
Cai, H., Gan, C., Zhu, L., and Han, S · 2020
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A baseline for few-shot image classification
Dhillon, G. S., Chaudhari, P., Ravichandran, A., and Soatto, S · 2020
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A Broader Study of Cross-Domain Few-Shot Learning
Guo, Y., Codella, N. C., Karlinsky, L., Codella, J. V., Smith, J. R., Saenko, K., Rosing, T., and Feris, R · 2020
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Pan, Z., Chen, P., He, H., Liu, J., Cai, J., and Zhuang, B · 2021
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TinyOL: TinyML with Online-Learning on Microcontrollers
Ren, H., Anicic, D., and Runkler, T. A · 2021
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AdapterDrop: On the efficiency of adapters in transformers
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Shallow Bayesian Meta Learning for Real-World Few-Shot Recognition
Zhang, X., Meng, D., Gouk, H., and Hospedales, T. M · 2021
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Memory-Efficient DNN Training on Mobile Devices
Gim, I. and Ko, J · 2022
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Meta-Learning in Neural Networks: A Survey
Hospedales, T., Antoniou, A., Micaelli, P., and Storkey, A · 2022
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Pushing the limits of simple pipelines for few-shot learning: External data and fine-tuning make a difference
Hu, S. X., Li, D., Stühmer, J., Kim, M., and Hospedales, T. M · 2022
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Band: Coordinated Multi-DNN Inference on Heterogeneous Mobile Processors
Jeong, J. S., Lee, J., Kim, D., Jeon, C., Jeong, C., Lee, Y., and Chun, B.-G · 2022
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Fisher SAM: Information Geometry and Sharpness Aware Minimisation
Kim, M., Li, D., Hu, S. X., and Hospedales, T · 2022
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Cross-domain few-shot learning with task-specific adapters
Li, W.-H., Liu, X., and Bilen, H · 2022
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On-Device Training Under 256KB Memory
Lin, J., Zhu, L., Chen, W.-M., Wang, W.-C., Gan, C., and Han, S · 2022
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BlastNet: Exploiting Duo-Blocks for Cross-Processor Real-Time DNN Inference
Ling, N., Huang, X., Zhao, Z., Guan, N., Yan, Z., and Xing, G · 2022
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GACT: Activation Compressed Training for Generic Network Architectures
Liu, X., Zheng, L., Wang, D., Cen, Y., Chen, W., Han, X., Chen, J., Liu, Z., Tang, J., Gonzalez, J., Mahoney, M., and Cheung, A · 2022
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POET: Training Neural Networks on Tiny Devices with Integrated Rematerialization and Paging
Patil, S. G., Jain, P., Dutta, P., Stoica, I., and Gonzalez, J · 2022
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The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
Patterson, D., Gonzalez, J., Hölzle, U., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D. R., Texier, M., and Dean, J · 2022
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MiniLearn: On-Device Learning for Low-Power IoT Devices
Profentzas, C., Almgren, M., and Landsiedel, O · 2022
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P-Meta: Towards On-Device Deep Model Adaptation
Qu, Z., Zhou, Z., Tong, Y., and Thiele, L · 2022
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Deep learning on microcontrollers: A study on deployment costs and challenges
Svoboda, F., Fernandez-Marques, J., Liberis, E., and Lane, N. D · 2022
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Melon: Breaking the Memory Wall for Resource-Efficient On-Device Machine Learning
Wang, Q., Xu, M., Jin, C., Dong, X., Yuan, J., Jin, X., Huang, G., Liu, Y., and Liu, X · 2022
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Mandheling: Mixed-Precision On-Device DNN Training with DSP Offloading
Xu, D., Xu, M., Wang, Q., Wang, S., Ma, Y., Huang, K., Huang, G., Jin, X., and Liu, X · 2022
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Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution, 2023
Liberis, E. and Lane, N. D · 2023
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