Fetching the paper…
Reading the bibliography…
This work presents a method for adapting a single, fixed deep neural network to multiple tasks without affecting performance on already learned tasks.
Multitask learning
Caruana, R.: · 1998
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
French, R.M.: · 1999
Earlier work this paper cites.
Automated flower classification over a large number of classes
Nilsback, M.E., Zisserman, A.: · 2008
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: · 2011
Earlier work this paper cites.
How do humans sketch objects?
Eitz, M., Hays, J., Alexa, M.: · 2012
Earlier work this paper cites.
3d object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., Fei-Fei, L.: · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: · 2015
Earlier work this paper cites.
Large-scale classification of fine-art paintings: Learning the right metric on the right feature
Saleh, B., Elgammal, A.: · 2015
Earlier work this paper cites.
Learning both weights and connections for efficient neural network
Han, S., Pool, J., Tran, J., Dally, W.: · 2015
Earlier work this paper cites.
Binaryconnect: Training deep neural networks with binary weights during propagations
Courbariaux, M., Bengio, Y., David, J.P.: · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Cited alongside, same era.
Learning without forgetting
Li, Z., Hoiem, D.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Wide residual networks
Zagoruyko, S., Komodakis, N.: · 2016
Cited alongside, same era.
Integrated perception with recurrent multi-task neural networks
Bilen, H., Vedaldi, A.: · 2016
Cited alongside, same era.
Rusu, A.A., Rabinowitz, N.C., Desjardins, G., Soyer, H., Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., Hadsell, R.: · 2016
Cited alongside, same era.
PackNet: Adding multiple tasks to a single network by iterative pruning
Mallya, A., Lazebnik, S.: · 2017
Later among the works it cites.
Densely connected convolutional networks
Huang, G., Liu, Z., van der Maaten, L., Weinberger, K.Q.: · 2017
Later among the works it cites.
Incremental learning through deep adaptation
Rosenfeld, A., Tsotsos, J.K.: · 2017
Later among the works it cites.
Learning multiple visual domains with residual adapters
Rebuffi, S.A., Bilen, H., Vedaldi, A.: · 2017
Later among the works it cites.
Ubernet: Training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory
Kokkinos, I.: · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Binarized neural networks
Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: · 2016
Cited alongside, same era.
Li, F., Zhang, B., Liu, B.: · 2016
Cited alongside, same era.
Dynamic network surgery for efficient dnns
Guo, Y., Yao, A., Chen, Y.: · 2016
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al.: · 2017
Cited alongside, same era.
Encoder based lifelong learning
Rannen, A., Aljundi, R., Blaschko, M.B., Tuytelaars, T.: · 2017
Cited alongside, same era.
Incremental learning of object detectors without catastrophic forgetting
Shmelkov, K., Schmid, C., Alahari, K.: · 2017
Later among the works it cites.
Overcoming catastrophic forgetting by incremental moment matching
Lee, S.W., Kim, J.H., Ha, J.W., Zhang, B.T.: · 2017
Later among the works it cites.
PathNet: Evolution channels gradient descent in super neural networks
Fernando, C., Banarse, D., Blundell, C., Zwols, Y., Ha, D., Rusu, A.A., Pritzel, A., Wierstra, D.: · 2017
Later among the works it cites.
Trained ternary quantization
Zhu, C., Han, S., Mao, H., Dally, W.J.: · 2017
Later among the works it cites.
Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A.: · 2017
Later among the works it cites.
Segmentation data splits
BerekeleyVision: · 2018
Closest in time.