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Generating sound effects that humans want is an important topic.
D. Griffin and J. Lim, “Signal estimation from modified short-time Fourier transform,” IEEE Transactions on Acoustics, Speech, and Signal Processing
1984
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th annual international conference on machine learning
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in IEEE conference on computer vision and pattern recognition
2009
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980
2014
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning
2015
Earlier work this paper cites.
R. Vedantam, C. Lawrence Zitnick, and D. Parikh, “CIDER: Consensus-based image description evaluation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2015
Earlier work this paper cites.
S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee, “Generative adversarial text to image synthesis,” in International Conference on Machine Learning
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2016
Earlier work this paper cites.
P. Anderson, B. Fernando, M. Johnson, and S. Gould, “SPICE: Semantic propositional image caption evaluation,” in European Conference on Computer Vision
2016
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450
2016
Earlier work this paper cites.
A. Van Den Oord, O. Vinyals, et al
2017
Earlier work this paper cites.
J. F. Gemmeke, D. P. Ellis, D. Freedman, A. Jansen, W. Lawrence, R. C. Moore, M. Plakal, and M. Ritter, “Audio Set: An ontology and human-labeled dataset for audio events,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in Neural Information Processing Systems
2017
Earlier work this paper cites.
Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio, et al
2017
Earlier work this paper cites.
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros, “Image-to-image translation with conditional adversarial networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Y. Zhou, Z. Wang, C. Fang, T. Bui, and T. L. Berg, “Visual to sound: Generating natural sound for videos in the wild,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Earlier work this paper cites.
Z. Zhang, Y. Xie, and L. Yang, “Photographic text-to-image synthesis with a hierarchically-nested adversarial network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding by generative pre-training,” 2018
2018
Cited alongside, same era.
N. Parmar, A. Vaswani, J. Uszkoreit, L. Kaiser, N. Shazeer, A. Ku, and D. Tran, “Image transformer,” in International Conference on Machine Learning
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Q. Kong, Y. Xu, T. Iqbal, Y. Cao, W. Wang, and M. D. Plumbley, “Acoustic scene generation with conditional SampleRNN,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019
Cited alongside, same era.
X. Liu, T. Iqbal, J. Zhao, Q. Huang, M. D. Plumbley, and W. Wang, “Conditional sound generation using neural discrete time-frequency representation learning,” in IEEE International Workshop on Machine Learning for Signal Processing (MLSP)
2021
Later among the works it cites.
J. Austin, D. Johnson, J. Ho, D. Tarlow, and R. van den Berg, “Structured denoising diffusion models in discrete state-spaces,” Advances in Neural Information Processing Systems
2021
Later among the works it cites.
2021
Later among the works it cites.
P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2021
Later among the works it cites.
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K. Kumar, R. Kumar, T. de Boissiere, L. Gestin, W. Z. Teoh, J. Sotelo, A. de Brébisson, Y. Bengio, and A. C. Courville, “Mel-GAN: Generative adversarial networks for conditional waveform synthesis,” Advances in Neural Information Processing Systems
2019
Cited alongside, same era.
C. D. Kim, B. Kim, H. Lee, and G. Kim, “AudioCaps: Generating captions for audios in the wild,” in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
A. Razavi, A. Van den Oord, and O. Vinyals, “Generating diverse high-fidelity images with VQ-VAE-2,” Advances in Neural Information Processing Systems
2019
Cited alongside, same era.
2019
Cited alongside, same era.
M. Wu, H. Dinkel, and K. Yu, “Audio caption: Listen and tell,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019
Cited alongside, same era.
P. Chen, Y. Zhang, M. Tan, H. Xiao, D. Huang, and C. Gan, “Generating visually aligned sound from videos,” IEEE Transactions on Image Processing
2020
Cited alongside, same era.
H. Chen, W. Xie, A. Vedaldi, and A. Zisserman, “VGGSound: A large-scale audio-visual dataset,” in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2020
Cited alongside, same era.
M. Ding, Z. Yang, W. Hong, W. Zheng, C. Zhou, D. Yin, J. Lin, X. Zou, Z. Shao, H. Yang, et al
2021
Later among the works it cites.
A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning
2021
Later among the works it cites.
P. Esser, R. Rombach, A. Blattmann, and B. Ommer, “ImageBART: Bidirectional context with multinomial diffusion for autoregressive image synthesis,” Advances in Neural Information Processing Systems
2021
Later among the works it cites.
P. Dhariwal and A. Nichol, “Diffusion models beat GANS on image synthesis,” Advances in Neural Information Processing Systems
2021
Later among the works it cites.
Z. Kong, W. Ping, J. Huang, K. Zhao, and B. Catanzaro, “DiffWave: A versatile diffusion model for audio synthesis,” International Conference on Learning Representations
2021
Later among the works it cites.
M. Jeong, H. Kim, S. J. Cheon, B. J. Choi, and N. S. Kim, “Diff-TTS: A denoising diffusion model for text-to-speech,” Proc. InterSpeech
2021
Later among the works it cites.
“Grad-TTS: A diffusion probabilistic model for text-to-speech,” in International Conference on Machine Learning
2021
Later among the works it cites.
E. Hoogeboom, D. Nielsen, P. Jaini, P. Forré, and M. Welling, “Argmax flows and multinomial diffusion: Towards non-autoregressive language models,” arXiv preprint
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al
2021
Later among the works it cites.
2021
Later among the works it cites.
A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in International Conference on Machine Learning
2021
Later among the works it cites.
2021
Later among the works it cites.
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” 2022
2022
Closest in time.
A. Q. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. Mcgrew, I. Sutskever, and M. Chen, “GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models,” in International Conference on Machine Learning
2022
Closest in time.