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There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.
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Mask R-CNN
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Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
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Feature Pyramid Networks for Object Detection
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Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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SGDR: Stochastic Gradient Descent with Warm Restarts
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Lossy Image Compression with Compressive Autoencoders
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Distilled Split Deep Neural Networks for Edge-Assisted Real-Time Systems
Yoshitomo Matsubara, Sabur Baidya, Davide Callegaro, Marco Levorato, and Sameer Singh · 2019
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PyTorch: An imperative style, high-performance deep learning library
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Full Resolution Image Compression with Recurrent Neural Networks
George Toderici, Damien Vincent, Nick Johnston, Sung Jin Hwang, David Minnen, Joel Shor, and Michele Covell · 2017
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Do deep convolutional nets really need to be deep and convolutional?
Gregor Urban, Krzysztof J Geras, Samira Ebrahimi Kahou, Ozlem Aslan, Shengjie Wang, Rich Caruana, Abdelrahman Mohamed, Matthai Philipose, and Matt Richardson · 2017
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Variational image compression with a scale hyperprior
Johannes Ballé, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston · 2018
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Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
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Joint Autoregressive and Hierarchical Priors for Learned Image Compression
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Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing Systems
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Improving Inference for Neural Image Compression
Yibo Yang, Robert Bamler, and Stephan Mandt · 2020
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Variational Bayesian Quantization
Yibo Yang, Robert Bamler, and Stephan Mandt · 2020
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BPG Image format
Fabrice Bellard · 2021
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Lossy Compression for Lossless Prediction
Yann Dubois, Benjamin Bloem-Reddy, Karen Ullrich, and Chris J Maddison · 2021
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Compression Techniques — WebP — Google Developers
Google · 2021
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Progressive Neural Image Compression with Nested Quantization and Latent Ordering
Yadong Lu, Yinhao Zhu, Yang Yang, Amir Said, and Taco S Cohen · 2021
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torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation
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Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks
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Learning Transferable Visual Models From Natural Language Supervision
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