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Transferability estimation has been an essential tool in selecting a pre-trained model and the layers in it for transfer learning, to transfer, so as to maximize the performance on a target task and prevent negative transfer.
A new measure of rank correlation
Kendall, M. G · 1938
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On the epsilon-entropy and the rate-distortion function of certain non-gaussian processes
Binia, J., Zakai, M., and Ziv, J · 1974
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Induction of decision trees
Quinlan, J. R · 1986
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Estimation of mutual information using kernel density estimators
Moon, Y.-I., Rajagopalan, B., and Lall, U · 1995
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Nonparametric entropy estimation: An overview
Beirlant, J., Dudewicz, E. J., Györfi, L., and Van der Meulen, E. C · 1997
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Elements of information theory
Cover, T. M · 1999
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The im algorithm: a variational approach to information maximization
Agakov, D. B. F · 2004
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Esol: estimating aqueous solubility directly from molecular structure
Delaney, J. S · 2004
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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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Estimating mutual information
Kraskov, A., Stögbauer, H., and Grassberger, P · 2004
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Caltech-256 object category dataset
Griffin, G., Holub, A., and Perona, P · 2007
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Segmentation of multivariate mixed data via lossy data coding and compression
Ma, Y., Derksen, H., Hong, W., and Wright, J · 2007
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
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A survey on transfer learning
Pan, S. J. and Yang, Q · 2009
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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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Chembl: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al · 2012
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A bayesian approach to in silico blood-brain barrier penetration modeling
Martins, I. F., Teixeira, A. L., Pinheiro, L., and Falcao, A. O · 2012
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Cats and dogs
Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C · 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
Cited alongside, same era.
Overcoming negative transfer: A survey
Zhang, W., Deng, L., and Wu, D · 2013
Cited alongside, same era.
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
Cited alongside, same era.
Food-101–mining discriminative components with random forests
Bossard, L., Guillaumin, M., and Van Gool, L · 2014
Cited alongside, same era.
Describing textures in the wild
Cimpoi, M., Maji, S., Kokkinos, I., Mohamed, S., and Vedaldi, A · 2014
Cited alongside, same era.
Representation similarity analysis for efficient task taxonomy & transfer learning
Dwivedi, K. and Roig, G · 2019
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y · 2019
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Rethinking softmax with cross-entropy: Neural network classifier as mutual information estimator
Qin, Z., Kim, D., and Gedeon, T · 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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Characterizing and avoiding negative transfer
Wang, Z., Dai, Z., Póczos, B., and Carbonell, J · 2019
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Mobley, D. L. and Guthrie, J. P · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
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., Bernstein, M., et al · 2015
Cited alongside, same era.
Zinc 15–ligand discovery for everyone
Sterling, T. and Irwin, J. J · 2015
Cited alongside, same era.
Deep learning and the information bottleneck principle
Tishby, N. and Zaslavsky, N · 2015
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Computational modeling of β \beta -secretase 1 (bace-1) inhibitors using ligand based approaches
Subramanian, G., Ramsundar, B., Pande, V., and Denny, R. A · 2016
Cited alongside, same era.
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., and Joulin, A · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
Grill, J.-B., Strub, F., Altché, F., Tallec, C., Richemond, P. H., Buchatskaya, E., Doersch, C., Pires, B. A., Guo, Z. D., Azar, M. G., et al · 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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Leep: A new measure to evaluate transferability of learned representations
Nguyen, C. V., Hassner, T., Archambeau, C., and Seeger, M · 2020
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Self-supervised graph transformer on large-scale molecular data
Rong, Y., Bian, Y., Xu, T., Xie, W., Wei, Y., Huang, W., and Huang, J · 2020
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Do adversarially robust imagenet models transfer better?
Salman, H., Ilyas, A., Engstrom, L., Kapoor, A., and Madry, A · 2020
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Neural joint entropy estimation
Shalev, Y., Painsky, A., and Ben-Gal, I · 2020
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Depara: Deep attribution graph for deep knowledge transferability
Song, J., Chen, Y., Ye, J., Wang, X., Shen, C., Mao, F., and Song, M · 2020
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Learning diverse and discriminative representations via the principle of maximal coding rate reduction
Yu, Y., Chan, K. H. R., You, C., Song, C., and Ma, Y · 2020
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A linearized framework and a new benchmark for model selection for fine-tuning
Deshpande, A., Achille, A., Ravichandran, A., Li, H., Zancato, L., Fowlkes, C., Bhotika, R., Soatto, S., and Perona, P · 2021
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Ranking neural checkpoints
Li, Y., Jia, X., Sang, R., Zhu, Y., Green, B., Wang, L., and Gong, B · 2021
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A mathematical framework for quantifying transferability in multi-source transfer learning
Tong, X., Xu, X., Huang, S.-L., and Zheng, L · 2021
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Logme: Practical assessment of pre-trained models for transfer learning
You, K., Liu, Y., Long, M., and Wang, J · 2021
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Quantifying and improving transferability in domain generalization
Zhang, G., Zhao, H., Yu, Y., and Poupart, P · 2021
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