Fetching the paper…
Reading the bibliography…
To learn models or features that generalize across tasks and domains is one of the grand goals of machine learning.
Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol · 2008
Earlier work this paper cites.
Parallel coordinate and parallel coordinate density plots
Rida E Moustafa · 2011
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
Earlier work this paper cites.
Detection of traffic signs in real-world images: The German Traffic Sign Detection Benchmark
Sebastian Houben, Johannes Stallkamp, Jan Salmen, Marc Schlipsing, and Christian Igel · 2013
Earlier work this paper cites.
Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
Earlier work this paper cites.
Distance-based image classification: Generalizing to new classes at near-zero cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2013
Earlier work this paper cites.
Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik · 2014
Earlier work this paper cites.
An efficient approach for assessing hyperparameter importance
F. Hutter, H. Hoos, and K. Leyton-Brown · 2014
Earlier work this paper cites.
Overfeat: Integrated recognition, localization and detection using convolutional networks
Pierre Sermanet, David Eigen, Xiang Zhang, Michaël Mathieu, Rob Fergus, and Yann LeCun · 2014
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A. Efros · 2015
Earlier work this paper cites.
Fast r-cnn
Ross Girshick · 2015
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
Gregory Koch, Richard Zemel, and Ruslan Salakhutdinov · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Non-stochastic best arm identification and hyperparameter optimization
Kevin Jamieson and Ameet Talwalkar · 2016
Earlier work this paper cites.
The quick, draw!-ai experiment
Jonas Jongejan, Henry Rowley, Takashi Kawashima, Jongmin Kim, and Nick Fox-Gieg · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
Earlier work this paper cites.
Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krähenbühl, Jeff Donahue, Trevor Darrell, and Alexei Efros · 2016
Earlier work this paper cites.
Taking the human out of the loop: A review of bayesian optimization
Bobak Shahriari, Kevin Swersky, Ziyu Wang, Ryan P. Adams, and Nando de Freitas · 2016
Cited alongside, same era.
Matching networks for one shot learning
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Cited alongside, same era.
Hyperparameter optimization of deep neural networks: Combining hyperband with Bayesian model selection
Hadrien Bertrand, Roberto Ardon, Matthieu Perrot, and Isabelle Bloch · 2017
Cited alongside, same era.
Improved regularization of convolutional neural networks with cutout
Terrance Devries and Graham W. Taylor · 2017
Cited alongside, same era.
Adversarial feature learning
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell · 2017
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Fgvcx fungi classification challenge 2018
Brigit Schroeder and Yin Cui · 2018
Later among the works it cites.
A bayesian perspective on generalization and stochastic gradient descent
Samuel L. Smith and Quoc V. Le · 2018
Later among the works it cites.
Learning to compare: Relation network for few-shot learning
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales · 2018
Later among the works it cites.
Representation learning with contrastive predictive coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
Later among the works it cites.
Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, X Yu Stella, and Dahua Lin · 2018
Later among the works it cites.
Towards automated deep learning: Efficient joint neural architecture and hyperparameter search
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
Cited alongside, same era.
Shake-shake regularization
Xavier Gastaldi · 2017
Cited alongside, same era.
Population based training of neural networks
Max Jaderberg, Valentin Dalibard, Simon Osindero, Wojciech M. Czarnecki, Jeff Donahue, Ali Razavi, Oriol Vinyals, Tim Green, Iain Dunning, Karen Simonyan, Chrisantha Fernando, and Koray Kavukcuoglu · 2017
Cited alongside, same era.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2017
Cited alongside, same era.
SGDR: stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
Cited alongside, same era.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
Cited alongside, same era.
Arber Zela, Aaron Klein, Stefan Falkner, and Frank Hutter · 2018
Later among the works it cites.
A closer look at few-shot classification
Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Wang, and Jia-Bin Huang · 2019
Later among the works it cites.
Autoaugment: Learning augmentation strategies from data
Ekin Dogus Cubuk, Barret Zoph, Dandelion Mané, V. Vasudevan, and Quoc V. Le · 2019
Later among the works it cites.
Randaugment: Practical data augmentation with no separate search
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2019
Later among the works it cites.
Large scale adversarial representation learning
Jeff Donahue and Karen Simonyan · 2019
Later among the works it cites.
Diversity with cooperation: Ensemble methods for few-shot classification
Nikita Dvornik, Cordelia Schmid, and Julien Mairal · 2019
Later among the works it cites.
Instance-level embedding adaptation for few-shot learning
Fusheng Hao, Jun Cheng, Lei Wang, and Jianzhong Cao · 2019
Later among the works it cites.
Population based augmentation: Efficient learning of augmentation policy schedules
Daniel Ho, Eric Liang, Xi Chen, Ion Stoica, and Pieter Abbeel · 2019
Later among the works it cites.
Meta-learning with differentiable convex optimization
Kwonjoon Lee, Subhransu Maji, Avinash Ravichandran, and Stefano Soatto · 2019
Later among the works it cites.
Fast autoaugment
Sungbin Lim, Ildoo Kim, Taesup Kim, Chiheon Kim, and Sungwoong Kim · 2019
Later among the works it cites.
Meta-learning with latent embedding optimization
Andrei A. Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell · 2019
Later among the works it cites.
Autodispnet: Improving disparity estimation with automl
Tonmoy Saikia, Yassine Marrakchi, Arber Zela, Frank Hutter, and Thomas Brox · 2019
Later among the works it cites.
A baseline for few-shot image classification
Guneet Singh Dhillon, Pratik Chaudhari, Avinash Ravichandran, and Stefano Soatto · 2020
Closest in time.
Fantastic generalization measures and where to find them
Yiding Jiang, Behnam Neyshabur, Dilip Krishnan, Hossein Mobahi, and Samy Bengio · 2020
Closest in time.
Meta-dataset: A dataset of datasets for learning to learn from few examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin, Pascal Lamblin, Utku Evci, Kelvin Xu, Ross Goroshin, Carles Gelada, Kevin Swersky, Pierre-Antoine Manzagol, and Hugo Larochelle · 2020
Closest in time.