GAN-Knowledge Distillation for one-stage Object Detection

Fitnets: Hints for thin deep nets

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Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang, Carlo Gatta, and Yoshua Bengio · 2014

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Distilling the knowledge in a neural network

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Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015

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Unsupervised representation learning with deep convolutional generative adversarial networks

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Faster r-cnn: Towards real-time object detection with region proposal networks

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R-fcn: Object detection via region-based fully convolutional networks

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Ssd: Single shot multibox detector

Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016

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Xnor-net: Imagenet classification using binary convolutional neural networks

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Quantized convolutional neural networks for mobile devices

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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer

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Sergey Zagoruyko and Nikos Komodakis · 2016

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Learning efficient object detection models with knowledge distillation

Guobin Chen, Wongun Choi, Xiang Yu, Tony Han, and Manmohan Chandraker · 2017

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Incremental learning of object detectors without catastrophic forgetting

Konstantin Shmelkov, Cordelia Schmid, and Karteek Alahari · 2017

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A gift from knowledge distillation: Fast optimization, network minimization and transfer learning

Junho Yim, Donggyu Joo, Jihoon Bae, and Junmo Kim · 2017

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Quantization and training of neural networks for efficient integer-arithmetic-only inference

Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018

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Shufflenet v2: Practical guidelines for efficient cnn architecture design

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Mobilenetv2: Inverted residuals and linear bottlenecks

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