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This paper studies task adaptive pre-trained model selection, an underexplored problem of assessing pre-trained models for the target task and select best ones from the model zoo \emph{without fine-tuning}.
A new measure of rank correlation
Kendall, M. G · 1938
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Developments in maximum entropy data analysis
Gull, S. F · 1989
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Learning to Learn: Introduction and Overview
Thrun, S. and Pratt, L · 1998
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Elements of information theory
Cover, T. M · 1999
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Exploiting task relatedness for multiple task learning
Ben-David, S. and Schuller, R · 2003
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Improved Baselines with Momentum Contrastive Learning
Chen, X., Fan, H., Girshick, R., and He, K · 2003
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Comparing top k lists
Fagin, R., Kumar, R., and Sivakumar, D · 2003
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Fei-Fei, L., Fergus, R., and Perona, P · 2004
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Pattern recognition and machine learning
Bishop, C. M · 2006
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Probabilistic graphical models: principles and techniques
Koller, D. and Friedman, N · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Dataset shift in machine learning
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., and Lawrence, N. D · 2009
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Why does unsupervised pre-training help deep learning?
Erhan, D., Courville, A., Bengio, Y., and Vincent, P · 2010
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
Gutmann, M. and Hyvärinen, A · 2010
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Sun database: Large-scale scene recognition from abbey to zoo
Xiao, J., Hays, J., Ehinger, K. A., Oliva, A., and Torralba, A · 2010
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Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C. V · 2012
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Collecting a large-scale dataset of fine-grained cars
Krause, J., Deng, J., Stark, M., and Fei-Fei, L · 2013
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Fine-Grained Visual Classification of Aircraft
Maji, S., Rahtu, E., Kannala, J., Blaschko, M., and Vedaldi, A · 2013
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Birdsnap: Large-scale fine-grained visual categorization of birds
Berg, T., Liu, J., Woo Lee, S., Alexander, M. L., Jacobs, D. W., and Belhumeur, P. N · 2014
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Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
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Decaf: A deep convolutional activation feature for generic visual recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., and Darrell, T · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
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How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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Unsupervised Domain Adaptation by Backpropagation
Ganin, Y. and Lempitsky, V · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Bayesian Evidence and Model Selection
Knuth, K. H., Habeck, M., Malakar, N. K., Mubeen, A. M., and Placek, B · 2015
Cited alongside, same era.
Learning Transferable Features with Deep Adaptation Networks
Long, M., Cao, Y., Wang, J., and Jordan, M · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., and Bernstein, M · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Do better imagenet models transfer better?
Kornblith, S., Shlens, J., and Le, Q. V · 2019
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Sanh, V., Debut, L., Chaumond, J., and Wolf, T · 2019
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Mnasnet: Platform-aware neural architecture search for mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V · 2019
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Transferability and hardness of supervised classification tasks
Tran, A. T., Nguyen, C. V., and Hassner, T · 2019
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A Weighted Correlation Index for Rankings with Ties
Vigna, S · 2015
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
SQuAD: 100,000+ Questions for Machine Comprehension of Text
Rajpurkar, P., Zhang, J., Lopyrev, K., and Liang, P · 2016
Cited alongside, same era.
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Daunizeau, J · 2017
Cited alongside, same era.
Xlnet: Generalized autoregressive pretraining for language understanding
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., and Le, Q. V · 2019
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ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Clark, K., Luong, M.-T., Le, Q. V., and Manning, C. D · 2020
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Tanda: Transfer and adapt pre-trained transformer models for answer sentence selection
Garg, S., Vu, T., and Moschitti, A · 2020
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Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., and Smith, N. A · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Strategies for Pre-training Graph Neural Networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 2020
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Self-supervised visual feature learning with deep neural networks: A survey
Jing, L. and Tian, Y · 2020
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Stochastic Normalization
Kou, Z., You, K., Long, M., and Wang, J · 2020
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ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., and Soricut, R · 2020
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Rethinking the Hyperparameters for Fine-tuning
Li, H., Chaudhari, P., Yang, H., Lam, M., Ravichandran, A., Bhotika, R., and Soatto, S · 2020
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What is being transferred in transfer learning?
Neyshabur, B., Sedghi, H., and Zhang, C · 2020
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LEEP: A New Measure to Evaluate Transferability of Learned Representations
Nguyen, C., Hassner, T., Seeger, M., and Archambeau, C · 2020
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Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?
Pruksachatkun, Y., Phang, J., Liu, H., Htut, P. M., Zhang, X., Pang, R. Y., Vania, C., Kann, K., and Bowman, S. R · 2020
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What Makes for Good Views for Contrastive Learning?
Tian, Y., Sun, C., Poole, B., Krishnan, D., Schmid, C., and Isola, P · 2020
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SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C. J., Polat, İ., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors · 2020
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Transformers: State-of-the-art natural language processing
Wolf, T., Chaumond, J., Debut, L., Sanh, V., Delangue, C., Moi, A., Cistac, P., Funtowicz, M., Davison, J., and Shleifer, S · 2020
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Co-Tuning for Transfer Learning
You, K., Kou, Z., Long, M., and Wang, J · 2020
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A Large-scale Study of Representation Learning with the Visual Task Adaptation Benchmark
Zhai, X., Puigcerver, J., Kolesnikov, A., Ruyssen, P., Riquelme, C., Lucic, M., Djolonga, J., Pinto, A. S., Neumann, M., Dosovitskiy, A., Beyer, L., Bachem, O., Tschannen, M., Michalski, M., Bousquet, O., Gelly, S., and Houlsby, N · 2020
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