Fetching the paper…
Reading the bibliography…
Zero-shot classification is a generalization task where no instance from the target classes is seen during training.
Learning for new visual environments with limited labels
Zhu, P., Wang, H., and Saligrama, V · 1901
Earlier work this paper cites.
Parts of recognition
Hoffman, D. D. and Richards, W. A · 1984
Earlier work this paper cites.
Recognition-by-components: a theory of human image understanding
Biederman, I · 1987
Earlier work this paper cites.
One-shot learning of object categories
Li, F.-F., Fergus, R., and Perona, P · 2006
Earlier work this paper cites.
Zero-data learning of new tasks
Larochelle, H., Erhan, D., and Bengio, Y · 2008
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Sun attribute database: Discovering, annotating, and recognizing scene attributes
Patterson, G. and Hays, J · 2012
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., and Lipson, H · 2014
Earlier work this paper cites.
Evaluation of output embeddings for fine-grained image classification
Akata, Z., Reed, S., Walter, D., Lee, H., and Schiele, B · 2015
Earlier work this paper cites.
Factors of transferability for a generic convnet representation
Azizpour, H., Razavian, A. S., Sullivan, J., Maki, A., and Carlsson, S · 2015
Earlier work this paper cites.
Makhzani, A., Shlens, J., Jaitly, N., and Goodfellow, I. J · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
Earlier work this paper cites.
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., Berg, A. C., and Fei-Fei, L · 2015
Earlier work this paper cites.
Synthesized classifiers for zero-shot learning
Changpinyo, S., Chao, W.-L., Gong, B., and Sha, F · 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.
What makes imagenet good for transfer learning?
Huh, M., Agrawal, P., and Efros, A. A · 2016
Cited alongside, same era.
Generative adversarial text to image synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H · 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.
Learning semantically and additively compositional distributional representations
Learning a deep embedding model for zero-shot learning
Zhang, L., Xiang, T., and Gong, S · 2017
Later among the works it cites.
Alet, F., Lozano-Pérez, T., and Kaelbling, L. P · 2018
Later among the works it cites.
Large scale fine-grained categorization and domain-specific transfer learning
Cui, Y., Song, Y., Sun, C., Howard, A., and Belongie, S · 2018
Later among the works it cites.
Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Trischler, A., and Bengio, Y · 2018
Later among the works it cites.
Bilinear attention networks
Kim, J.-H., Jun, J., and Zhang, B.-T · 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…
Tian, R., Okazaki, N., and Inui, K · 2016
Cited alongside, same era.
Matching networks for one shot learning
Vinyals, O., Blundell, C., Lillicrap, T. P., Kavukcuoglu, K., and Wierstra, D · 2016
Cited alongside, same era.
Model-agnostic meta-learning for fast adaptation of deep networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Fine-grained image classification via combining vision and language
He, X. and Peng, Y · 2017
Cited alongside, same era.
Semantic autoencoder for zero-shot learning
Kodirov, E., Xiang, T., and Gong, S · 2017
Cited alongside, same era.
Image caption with global-local attention
Li, L., Tang, S., Deng, L., Zhang, Y., and Tian, Q · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R. S · 2017
Cited alongside, same era.
Sun, M., Yuan, Y., Zhou, F., and Ding, E · 2018
Later among the works it cites.
Learning to compare: Relation network for few-shot learning
Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P. H., and Hospedales, T. M · 2018
Later among the works it cites.
Learning compositional representations for few-shot recognition
Tokmakov, P., Wang, Y.-X., and Hebert, M · 2018
Later among the works it cites.
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2018
Later among the works it cites.
Zero-shot learning-a comprehensive evaluation of the good, the bad and the ugly
Xian, Y., Lampert, C. H., Schiele, B., and Akata, Z · 2018
Later among the works it cites.
Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 2019
Later among the works it cites.
Systematic generalization: What is required and can it be learned?
Bahdanau, D., Murty, S., Noukhovitch, M., Nguyen, T. H., de Vries, H., and Courville, A · 2019
Later among the works it cites.
Centroid networks for few-shot clustering and unsupervised few-shot classification
Huang, G., Larochelle, H., and Lacoste-Julien, S · 2019
Later among the works it cites.
An analysis of pre-training on object detection, 2019
Li, H., Singh, B., Najibi, M., Wu, Z., and Davis, L. S · 2019
Later among the works it cites.