Fetching the paper…
Reading the bibliography…
Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest.
Which tasks should be learned together in multi-task learning?
Standley, T., Zamir, A. R., Chen, D., Guibas, L., Malik, J., and Savarese, S. (2019) · 1905
Earlier work this paper cites.
Design and regularization of neural networks: the optimal use of a validation set
Larsen, J., Hansen, L. K., Svarer, C., and Ohlsson, M. (1996) · 1996
Earlier work this paper cites.
Improving semantic analysis on point clouds via auxiliary supervision of local geometric priors
Tang, L., Chen, K., Wu, C., Hong, Y., Jia, K., and Yang, Z. (2020) · 2001
Earlier work this paper cites.
Self-supervised learning for domain adaptation on point-clouds
Achituve, I., Maron, H., and Chechik, G. (2020) · 2003
Earlier work this paper cites.
Efficient multiple hyperparameter learning for log-linear models
Foo, C.-s., Do, C. B., and Ng, A. Y. (2008) · 2008
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) · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al. (2009) · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y. (2011) · 2011
Earlier work this paper cites.
The Caltech-UCSD Birds-200-2011 Dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S. (2011) · 2011
Earlier work this paper cites.
Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C. V. (2012) · 2012
Earlier work this paper cites.
Indoor segmentation and support inference from RGBD images
Silberman, N., Hoiem, D., Kohli, P., and Fergus, R. (2012) · 2012
Earlier work this paper cites.
Indoor semantic segmentation using depth information
Couprie, C., Farabet, C., Najman, L., and LeCun, Y. (2013) · 2013
Earlier work this paper cites.
3D object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., and Fei-Fei, L. (2013) · 2013
Earlier work this paper cites.
ADAM: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
Earlier work this paper cites.
Exact solutions to the nonlinear dynamics of learning in deep linear neural network
Saxe, A. M., Mcclelland, J. L., and Ganguli, S. (2014) · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A. (2014) · 2014
Earlier work this paper cites.
Facial landmark detection by deep multi-task learning
Zhang, Z., Luo, P., Loy, C. C., and Tang, X. (2014) · 2014
Earlier work this paper cites.
Shapenet: An information-rich 3d model repository
Chang, A. X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al. (2015) · 2015
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Doersch, C., Gupta, A., and Efros, A. A. (2015) · 2015
Cited alongside, same era.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
Eigen, D. and Fergus, R. (2015) · 2015
Cited alongside, same era.
Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V. (2015) · 2015
Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016) · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
Cited alongside, same era.
On the optimization of deep networks: Implicit acceleration by overparameterization
Arora, S., Cohen, N., and Hazan, E. (2018) · 2018
Later among the works it cites.
Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Chen, Z., Badrinarayanan, V., Lee, C.-Y., and Rabinovich, A. (2018) · 2018
Later among the works it cites.
Adapting auxiliary losses using gradient similarity
Du, Y., Czarnecki, W. M., Jayakumar, S. M., Pascanu, R., and Lakshminarayanan, B. (2018) · 2018
Later among the works it cites.
Unsupervised representation learning by predicting image rotations
Gidaris, S., Singh, P., and Komodakis, N. (2018) · 2018
Later among the works it cites.
Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Kendall, A., Gal, Y., and Cipolla, R. (2018) · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D., and Kavukcuoglu, K. (2016) · 2016
Cited alongside, same era.
Scalable gradient-based tuning of continuous regularization hyperparameters
Luketina, J., Berglund, M., Greff, K., and Raiko, T. (2016) · 2016
Cited alongside, same era.
Unsupervised learning of visual representations by solving jigsaw puzzles
Noroozi, M. and Favaro, P. (2016) · 2016
Cited alongside, same era.
Hyperparameter optimization with approximate gradient
Pedregosa, F. (2016) · 2016
Cited alongside, same era.
Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Salimans, T. and Kingma, D. P. (2016) · 2016
Cited alongside, same era.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al. (2016) · 2016
Cited alongside, same era.
A scalable active framework for region annotation in 3D shape collections
Yi, L., Kim, V. G., Ceylan, D., Shen, I.-C., Yan, M., Su, H., Lu, C., Huang, Q., Sheffer, A., and Guibas, L. (2016) · 2016
Cited alongside, same era.
Reviving and improving recurrent back-propagation
Liao, R., Xiong, Y., Fetaya, E., Zhang, L., Yoon, K., Pitkow, X., Urtasun, R., and Zemel, R. (2018) · 2018
Later among the works it cites.
Multi-task learning as multi-objective optimization
Sener, O. and Koltun, V. (2018) · 2018
Later among the works it cites.
Learning longer-term dependencies in RNNs with auxiliary losses
Trinh, T., Dai, A., Luong, T., and Le, Q. (2018) · 2018
Later among the works it cites.
Scaling and benchmarking self-supervised visual representation learning
Goyal, P., Mahajan, D., Gupta, A., and Misra, I. (2019) · 2019
Later among the works it cites.
Unsupervised multi-task feature learning on point clouds
Hassani, K. and Haley, M. (2019) · 2019
Later among the works it cites.
Adaptive auxiliary task weighting for reinforcement learning
Lin, X., Baweja, H., Kantor, G., and Held, D. (2019) · 2019
Later among the works it cites.
Learning to navigate
Mirowski, P. (2019) · 2019
Later among the works it cites.
Meta-learning with implicit gradients
Rajeswaran, A., Finn, C., Kakade, S. M., and Levine, S. (2019) · 2019
Later among the works it cites.
Self-supervised deep learning on point clouds by reconstructing space
Sauder, J. and Sievers, B. (2019) · 2019
Later among the works it cites.
Dynamic graph cnn for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., and Solomon, J. M. (2019) · 2019
Later among the works it cites.
Self-supervised visual feature learning with deep neural networks: A survey
Jing, L. and Tian, Y. (2020) · 2020
Closest in time.
Optimizing millions of hyperparameters by implicit differentiation
Lorraine, J., Vicol, P., and Duvenaud, D. (2020) · 2020
Closest in time.
Multi-task self-supervised visual learning
Doersch, C. and Zisserman, A. (2017) · 2060
Closest in time.