Fetching the paper…
Reading the bibliography…
This work investigates dataset vectorization for two dataset-level tasks: assessing training set suitability and test set difficulty.
Sur la distance de deux lois de probabilité
Maurice Fréchet · 1957
Earlier work this paper cites.
Some methods for classification and analysis of multivariate observations
James MacQueen et al · 1967
Earlier work this paper cites.
On the histogram as a density estimator: L 2 theory
David Freedman and Persi Diaconis · 1981
Earlier work this paper cites.
A sequential algorithm for training text classifiers
David D Lewis and William A Gale · 1994
Earlier work this paper cites.
Similarity-based methods for word sense disambiguation
Ido Dagan, Lillian Lee, and Fernando Pereira · 1997
Earlier work this paper cites.
Webwatcher: A tour guide for the world wide web
Thorsten Joachims, Dayne Freitag, Tom Mitchell, et al · 1997
Earlier work this paper cites.
Text categorization with support vector machines: Learning with many relevant features
Thorsten Joachims · 1998
Earlier work this paper cites.
A comparison of event models for naive bayes text classification
Andrew McCallum, Kamal Nigam, et al · 1998
Earlier work this paper cites.
A weighted kendall’s tau statistic
Grace S Shieh · 1998
Earlier work this paper cites.
Centroidal voronoi tessellations: Applications and algorithms
Qiang Du, Vance Faber, and Max Gunzburger · 1999
Earlier work this paper cites.
Object recognition from local scale-invariant features
D.G. Lowe · 1999
Earlier work this paper cites.
Foundations of statistical natural language processing
Christopher Manning and Hinrich Schutze · 1999
Earlier work this paper cites.
Video google: a text retrieval approach to object matching in videos
Sivic and Zisserman · 2003
Earlier work this paper cites.
Visual categorization with bags of keypoints
Gabriella Csurka, Christopher Dance, Lixin Fan, Jutta Willamowski, and Cédric Bray · 2004
Earlier work this paper cites.
A bayesian hierarchical model for learning natural scene categories
L. Fei-Fei and P. Perona · 2005
Earlier work this paper cites.
Surf: Speeded up robust features
Herbert Bay, Tinne Tuytelaars, and Luc Van Gool · 2006
Earlier work this paper cites.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2006
Earlier work this paper cites.
A kernel method for the two-sample-problem
Arthur Gretton, Karsten Borgwardt, Malte Rasch, Bernhard Schölkopf, and Alex Smola · 2006
Earlier work this paper cites.
Scalable recognition with a vocabulary tree
David Nister and Henrik Stewenius · 2006
Earlier work this paper cites.
Fisher kernels on visual vocabularies for image categorization
Florent Perronnin and Christopher Dance · 2007
Earlier work this paper cites.
Object retrieval with large vocabularies and fast spatial matching
James Philbin, Ondrej Chum, Michael Isard, Josef Sivic, and Andrew Zisserman · 2007
Earlier work this paper cites.
Spatial tessellations: concepts and applications of voronoi diagrams
Barry Boots, Kokichi Sugihara, Sung Nok Chiu, and Atsuyuki Okabe · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky and Geoffrey Hinton · 2009
Earlier work this paper cites.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Vaughan · 2010
Earlier work this paper cites.
Aggregating local descriptors into a compact image representation
Hervé Jégou, Matthijs Douze, Cordelia Schmid, and Patrick Pérez · 2010
Earlier work this paper cites.
Improving the fisher kernel for large-scale image classification
Florent Perronnin, Jorge Sánchez, and Thomas Mensink · 2010
Earlier work this paper cites.
Robust statistics
Peter J Huber · 2011
Earlier work this paper cites.
Mining of massive datasets
Anand Rajaraman and Jeffrey David Ullman · 2011
Earlier work this paper cites.
Negative evidences and co-occurences in image retrieval: The benefit of pca and whitening
Hervé Jégou and Ondřej Chum · 2012
Cited alongside, same era.
Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
Cited alongside, same era.
Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
Cited alongside, same era.
A weighted correlation index for rankings with ties
Sebastiano Vigna · 2015
Cited alongside, same era.
A wasserstein-type distance in the space of gaussian mixture models
Julie Delon and Agnes Desolneux · 2020
Later among the works it cites.
Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
Later among the works it cites.
Transfer-learning-library
Junguang Jiang, Baixu Chen, Bo Fu, and Mingsheng Long · 2020
Later among the works it cites.
Characterizing structural regularities of labeled data in overparameterized models
Ziheng Jiang, Chiyuan Zhang, Kunal Talwar, and Michael C Mozer · 2020
Later among the works it cites.
Domain2vec: Domain embedding for unsupervised domain adaptation
Xingchao Peng, Yichen Li, and Kate Saenko · 2020
Later among the works it cites.
Dataset cartography: Mapping and diagnosing datasets with training dynamics
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Return of frustratingly easy domain adaptation
Baochen Sun, Jiashi Feng, and Kate Saenko · 2016
Cited alongside, same era.
Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Cited alongside, same era.
Sift meets cnn: A decade survey of instance retrieval
Liang Zheng, Yi Yang, and Qi Tian · 2017
Cited alongside, same era.
Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A Smith, and Yejin Choi · 2020
Later among the works it cites.
f-domain adversarial learning: Theory and algorithms
David Acuna, Guojun Zhang, Marc T Law, and Sanja Fidler · 2021
Later among the works it cites.
Deep learning through the lens of example difficulty
Robert Baldock, Hartmut Maennel, and Behnam Neyshabur · 2021
Later among the works it cites.
Detecting errors and estimating accuracy on unlabeled data with self-training ensembles
Jiefeng Chen, Frederick Liu, Besim Avci, Xi Wu, Yingyu Liang, and Somesh Jha · 2021
Later among the works it cites.
What does rotation prediction tell us about classifier accuracy under varying testing environments?
Weijian Deng, Stephen Gould, and Liang Zheng · 2021
Later among the works it cites.
Are labels always necessary for classifier accuracy evaluation?
Weijian Deng and Liang Zheng · 2021
Later among the works it cites.
Are labels always necessary for classifier accuracy evaluation
Weijian Deng and Liang Zheng · 2021
Later among the works it cites.
Repvgg: Making vgg-style convnets great again
Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, and Jian Sun · 2021
Later among the works it cites.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
Later among the works it cites.
Predicting with confidence on unseen distributions
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, and Ludwig Schmidt · 2021
Later among the works it cites.
On interaction between augmentations and corruptions in natural corruption robustness
Eric Mintun, Alexander Kirillov, and Saining Xie · 2021
Later among the works it cites.
Data and its (dis)contents: A survey of dataset development and use in machine learning research
Amandalynne Paullada, Inioluwa Deborah Raji, Emily Bender, Emily Denton, and Alex Hanna · 2021
Later among the works it cites.
Otce: A transferability metric for cross-domain cross-task representations
Yang Tan, Yang Li, and Shao-Lun Huang · 2021
Later among the works it cites.
How stable are transferability metrics evaluations?
Andrea Agostinelli, Michal Pándy, Jasper Uijlings, Thomas Mensink, and Vittorio Ferrari · 2022
Later among the works it cites.
Transferability metrics for selecting source model ensembles
Andrea Agostinelli, Jasper Uijlings, Thomas Mensink, and Vittorio Ferrari · 2022
Later among the works it cites.
Data excellence for ai: why should you care?
Lora Aroyo, Matthew Lease, Praveen Paritosh, and Mike Schaekermann · 2022
Later among the works it cites.
dcbench: A benchmark for data-centric ai systems
Sabri Eyuboglu, Bojan Karlaš, Christopher Ré, Ce Zhang, and James Zou · 2022
Later among the works it cites.
Stylegan-human: A data-centric odyssey of human generation
Jianglin Fu, Shikai Li, Yuming Jiang, Kwan-Yee Lin, Chen Qian, Chen Change Loy, Wayne Wu, and Ziwei Liu · 2022
Later among the works it cites.
Leveraging unlabeled data to predict out-of-distribution performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary C Lipton, Behnam Neyshabur, and Hanie Sedghi · 2022
Later among the works it cites.
Leveraging unlabeled data to predict out-of-distribution performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary Chase Lipton, Behnam Neyshabur, and Hanie Sedghi · 2022
Later among the works it cites.
Advances, challenges and opportunities in creating data for trustworthy ai
Weixin Liang, Girmaw Abebe Tadesse, Daniel Ho, L Fei-Fei, Matei Zaharia, Ce Zhang, and James Zou · 2022
Later among the works it cites.
Dataperf: Benchmarks for data-centric ai development
Mark Mazumder, Colby Banbury, Xiaozhe Yao, Bojan Karlaš, William Gaviria Rojas, Sudnya Diamos, Greg Diamos, Lynn He, Douwe Kiela, David Jurado, et al · 2022
Later among the works it cites.
Transferability estimation using bhattacharyya class separability
Michal Pándy, Andrea Agostinelli, Jasper Uijlings, Vittorio Ferrari, and Thomas Mensink · 2022
Later among the works it cites.
Voronoi density estimator for high-dimensional data: Computation, compactification and convergence
Vladislav Polianskii, Giovanni Luca Marchetti, Alexander Kravberg, Anastasiia Varava, Florian T Pokorny, and Danica Kragic · 2022
Later among the works it cites.