Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs, IEEE transactions on pattern analysis and machine intelligence, vol.40, pp.834-848, 2017. ,
Compressing neural networks with the hashing trick, Proceedings of the 32nd International Conference on Machine Learning, 2015. ,
A survey of model compression and acceleration for deep neural networks, 2017. ,
Data fine-tuning, Conference on Artificial Intelligence (AAAI), 2019. ,
A guide to convolution arithmetic for deep learning, 2016. ,
Why does unsupervised pretraining help deep learning, Journal of Machine Learning Research, vol.11, p.1, 2010. ,
Domain-adversarial training of neural networks, Journal of Machine Learning Research, vol.17, issue.2, pp.2096-2030, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01624607
Ultimate tensorization: compressing convolutional and fc layers alike, ArXiv, issue.1, 2016. ,
Endto-end learning of deep visual representations for image retrieval, 2016. ,
Spottune: Transfer learning through adaptive fine-tuning, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. ,
Learning both weights and connections for efficient neural network, Advances in Neural Information Processing Systems (NIPS), pp.1135-1143, 2015. ,
Mask r-cnn, Proceedings of the IEEE international conference on computer vision, pp.2961-2969, 2017. ,
Channel pruning for accelerating very deep neural networks, Proceedings of the IEEE International Conference on Computer Vision, pp.1389-1397, 2017. ,
Squeeze-and-excitation networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017. ,
Labeled optical coherence tomography (oct) and chest x-ray images for classification, vol.2, 2018. ,
Oseledets. Speeding-up convolutional neural networks using fine-tuned cp-decomposition, vol.1, 2014. ,
Optimal brain damage, Advances in neural information processing systems, pp.598-605, 1990. ,
Learning transferable features with deep adaptation networks, Proceedings of the 32nd International Conference on International Conference on Machine Learning (ICML), pp.97-105, 2015. ,
Pruning convolutional neural networks for resource efficient transfer learning, vol.1, 2017. ,
Deep model compression for mobile platforms: A survey, Tsinghua Science and Technology, vol.24, issue.2, 2019. ,
Tensorizing neural networks, NIPS, vol.1, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01237600
Domain adaptation via transfer component analysis, IEEE Transactions on Neural Networks, vol.22, issue.2, p.1, 2010. ,
Extreme network compression via filter group approximation, Proceedings of the European Conference on Computer Vision (ECCV), pp.300-316, 2018. ,
Object retrieval with large vocabularies and fast spatial matching, 2007 IEEE conference on computer vision and pattern recognition, pp.1-8, 2007. ,
Lost in quantization: Improving particular object retrieval in large scale image databases, 2008 IEEE conference on computer vision and pattern recognition, pp.1-8, 2008. ,
Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection, Proceedings of the 8th ACM on Multimedia Systems Conference, MMSys'17, pp.164-169, 2017. ,
Recognizing indoor scenes, 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp.413-420, 2009. ,
Cnn image retrieval learns from bow: Unsupervised fine-tuning with hard examples, European conference on computer vision, pp.3-20, 2016. ,
Revisiting oxford and paris: Large-scale image retrieval benchmarking, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018. ,
You only look once: Unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, pp.779-788, 2016. ,
Faster r-cnn: Towards real-time object detection with region proposal networks, Advances in neural information processing systems, pp.91-99, 2015. ,
Fosnet: An end-toend trainable deep neural network for scene recognition, 2019. ,
And the bit goes down: Revisiting the quantization of neural networks, In ICLR, vol.2020, issue.2 ,
URL : https://hal.archives-ouvertes.fr/hal-02434572
Correlation Alignment for Unsupervised Domain Adaptation, pp.153-171, 2017. ,
Metatransfer learning for few-shot learning, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019. ,
Convolutional neural networks for medical image analysis: Full training or fine tuning?, IEEE transactions on medical imaging, vol.35, issue.1, pp.1299-1312, 2016. ,
Particular object retrieval with integral max-pooling of CNN activations, vol.5, p.6, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01842218
Adversarial discriminative domain adaptation, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
Approximation with kronecker products, Linear algebra for large scale and real-time applications, pp.293-314, 1993. ,
Residual attention network for image classification, pp.6450-6458, 2017. ,
Joon-Young Lee, and In-So Kweon. Cbam: Convolutional block attention module, ECCV, 2018. ,
Adapting svm classifiers to data with shifted distributions, Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007), pp.69-76, 2007. ,
Transfer learning via learning to transfer, International Conference on Machine Learning, pp.5072-5081, 2018. ,
How transferable are features in deep neural networks?, Proceedings of the 27th International Conference on Neural Information Processing Systems (NIPS), pp.3320-3328, 2014. ,
On compressing deep models by low rank and sparse decomposition, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017. ,
Taskonomy: Disentangling task transfer learning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.3712-3722, 2018. ,
Transfer adaptation learning: A decade survey, 2019. ,
Variational convolutional neural network pruning, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2002. ,
Finetuning convolutional neural networks for biomedical image analysis: Actively and incrementally, CVPR, pp.4761-4772, 2017. ,