Fetching the paper…
Reading the bibliography…
An important milestone for AI is the development of algorithms that can produce drawings that are indistinguishable from those of humans.
Measurement of intelligence by drawings
Goodenough, F. L · 1926
Earlier work this paper cites.
Psychological evaluation of children’s human figure drawings
Koppitz, E. M · 1968
Earlier work this paper cites.
Origins of the modern mind: Three stages in the evolution of culture and cognition
Donald, M · 1991
Earlier work this paper cites.
The structure of perceptual categories
Feldman, J · 1997
Earlier work this paper cites.
A Bayesian framework for concept learning
Tenenbaum, J. B · 1999
Earlier work this paper cites.
Use of children’s drawings for measurement of developmental level and emotional status
Ryan-Wenger, N · 2001
Earlier work this paper cites.
Visual features of intermediate complexity and their use in classification
Ullman, S., Vidal-Naquet, M., and Sali, E · 2002
Earlier work this paper cites.
Fragment-based learning of visual object categories
Hegdé, J., Bart, E., and Kersten, D · 2008
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
Earlier work this paper cites.
Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al · 2015
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
Edwards, H. and Storkey, A · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
Cited alongside, same era.
The quick, draw!-ai experiment
Jongejan, J., Rowley, H., Kawashima, T., Kim, J., and Fox-Gieg, N · 2016
Cited alongside, same era.
One-shot generalization in deep generative models
Rezende, D., Danihelka, I., Gregor, K., Wierstra, D., et al · 2016
Cited alongside, same era.
Data augmentation generative adversarial networks
Antoniou, A., Storkey, A., and Edwards, H · 2017
Cited alongside, same era.
What are the visual features underlying human versus machine vision?
Linsley, D., Eberhardt, S., Sharma, T., Gupta, P., and Serre, T · 2017
Cited alongside, same era.
Bond-Taylor, S., Leach, A., Long, Y., and Willcocks, C. G · 2021
Later among the works it cites.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Later among the works it cites.
Look at the variance! efficient black-box explanations with sobol-based sensitivity analysis
Fel, T., Cadene, R., Chalvidal, M., Cord, M., Vigouroux, D., and Serre, T · 2021
Later among the works it cites.
Hierarchical few-shot generative models
Giannone, G. and Winther, O · 2021
Later among the works it cites.
Vision transformer for small-size datasets
Lee, S. H., Lee, S., and Song, B. C · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
Cited alongside, same era.
Prototypical networks for few-shot learning
Snell, J., Swersky, K., and Zemel, R · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
Learning what and where to attend
Linsley, D., Shiebler, D., Eberhardt, S., and Serre, T · 2018
Cited alongside, same era.
The omniglot challenge: a 3-year progress report
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2019
Cited alongside, same era.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 2019
Cited alongside, same era.
Glide: Towards photorealistic image generation and editing with text-guided diffusion models
Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., Sutskever, I., and Chen, M · 2021
Later among the works it cites.
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I · 2021
Later among the works it cites.
Diversity vs. recognizability: Human-like generalization in one-shot generative models
Boutin, V., Singhal, L., Thomas, X., and Serre, T · 2022
Later among the works it cites.
Exploring transformer backbones for image diffusion models
Chahal, P · 2022
Later among the works it cites.
Giannone, G., Nielsen, D., and Winther, O · 2022
Later among the works it cites.
Classifier-free diffusion guidance
Ho, J. and Salimans, T · 2022
Later among the works it cites.
A convnet for the 2020s
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S · 2022
Later among the works it cites.
Human alignment of neural network representations
Muttenthaler, L., Dippel, J., Linhardt, L., Vandermeulen, R. A., and Kornblith, S · 2022
Later among the works it cites.
Making sense of dependence: Efficient black-box explanations using dependence measure
Novello, P., Fel, T., and Vigouroux, D · 2022
Later among the works it cites.
Scalable diffusion models with transformers
Peebles, W. and Xie, S · 2022
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
Later among the works it cites.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., et al · 2022
Later among the works it cites.
One-shot generalization in humans revealed through a drawing task
Tiedemann, H., Morgenstern, Y., Schmidt, F., and Fleming, R. W · 2022
Later among the works it cites.