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Diffusion models (DMs) have shown great potential for high-quality image synthesis.
Generating long sequences with sparse transformers
Child, R.; Gray, S.; Radford, A.; and Sutskever, I. 2019 · 1904
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Yolov4: Optimal speed and accuracy of object detection
Bochkovskiy, A.; Wang, C.-Y.; and Liao, H.-Y. M. 2020 · 2004
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Statistical significance tests for machine translation evaluation
Koehn, P. 2004 · 2004
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Image quality assessment: from error visibility to structural similarity
Wang, Z.; Bovik, A. C.; Sheikh, H. R.; and Simoncelli, E. P. 2004 · 2004
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Image quality metrics: PSNR vs. SSIM
Hore, A.; and Ziou, D. 2010 · 2010
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Improving the fairness of deep generative models without retraining
Tan, S.; Shen, Y.; and Zhou, B. 2020 · 2012
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Nice: Non-linear independent components estimation
Dinh, L.; Krueger, D.; and Bengio, Y. 2014 · 2014
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2014 · 2014
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Microsoft coco: Common objects in context
Lin, T.-Y.; Maire, M.; Belongie, S.; Hays, J.; Perona, P.; Ramanan, D.; Dollár, P.; and Zitnick, C. L. 2014 · 2014
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Draw: A recurrent neural network for image generation
Gregor, K.; Danihelka, I.; Graves, A.; Rezende, D.; and Wierstra, D. 2015 · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A.; Metz, L.; and Chintala, S. 2015 · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O.; Fischer, P.; and Brox, T. 2015 · 2015
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J.; Weiss, E.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
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Learning structured output representation using deep conditional generative models
Sohn, K.; Lee, H.; and Yan, X. 2015 · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F.; Seff, A.; Zhang, Y.; Song, S.; Funkhouser, T.; and Xiao, J. 2015 · 2015
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Generating images from captions with attention
Mansimov, E.; Parisotto, E.; Ba, J. L.; and Salakhutdinov, R. 2016 · 2016
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Improved techniques for training gans
Salimans, T.; Goodfellow, I.; Zaremba, W.; Cheung, V.; Radford, A.; and Chen, X. 2016 · 2016
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Conditional image generation with pixelcnn decoders
Van den Oord, A.; Kalchbrenner, N.; Espeholt, L.; Vinyals, O.; Graves, A.; et al. 2016 · 2016
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Pixel recurrent neural networks
Van Oord, A.; Kalchbrenner, N.; and Kavukcuoglu, K. 2016 · 2016
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Density estimation using real nvp
Dinh, L.; Sohl-Dickstein, J.; and Bengio, S. 2017 · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M.; Ramsauer, H.; Unterthiner, T.; Nessler, B.; and Hochreiter, S. 2017 · 2017
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Arbitrary style transfer in real-time with adaptive instance normalization
Huang, X.; and Belongie, S. 2017 · 2017
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Progressive growing of gans for improved quality, stability, and variation
Karras, T.; Aila, T.; Laine, S.; and Lehtinen, J. 2017 · 2017
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Visual genome: Connecting language and vision using crowdsourced dense image annotations
Krishna, R.; Zhu, Y.; Groth, O.; Johnson, J.; Hata, K.; Kravitz, J.; Chen, S.; Kalantidis, Y.; Li, L.-J.; Shamma, D. A.; et al. 2017 · 2017
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Neural discrete representation learning
Van Den Oord, A.; Vinyals, O.; et al. 2017 · 2017
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Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks
Zhang, H.; Xu, T.; Li, H.; Zhang, S.; Wang, X.; Huang, X.; and Metaxas, D. N. 2017 · 2017
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The malicious use of artificial intelligence: Forecasting, prevention, and mitigation
Brundage, M.; Avin, S.; Clark, J.; Toner, H.; Eckersley, P.; Garfinkel, B.; Dafoe, A.; Scharre, P.; Zeitzoff, T.; Filar, B.; et al. 2018 · 2018
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Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
Elfwing, S.; Uchibe, E.; and Doya, K. 2018 · 2018
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Image generation from scene graphs
Johnson, J.; Gupta, A.; and Li, F.-F. 2018 · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P.; and Dhariwal, P. 2018 · 2018
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Assessing generative models via precision and recall
Sajjadi, M. S.; Bachem, O.; Lucic, M.; Bousquet, O.; and Gelly, S. 2018 · 2018
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Attngan: Fine-grained text to image generation with attentional generative adversarial networks
Xu, T.; Zhang, P.; Huang, Q.; Zhang, H.; Gan, Z.; Huang, X.; and He, X. 2018 · 2018
Cited alongside, same era.
Stackgan++: Realistic image synthesis with stacked generative adversarial networks
Zhang, H.; Xu, T.; Li, H.; Zhang, S.; Wang, X.; Huang, X.; and Metaxas, D. N. 2018 · 2018
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Large scale GAN training for high fidelity natural image synthesis
Brock, A.; Donahue, J.; and Simonyan, K. 2019 · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Clipscore: A reference-free evaluation metric for image captioning
Hessel, J.; Holtzman, A.; Forbes, M.; Bras, R. L.; and Choi, Y. 2021 · 2021
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High-Resolution Complex Scene Synthesis with Transformers
Jahn, M.; Rombach, R.; and Ommer, B. 2021 · 2021
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Alias-free generative adversarial networks
Karras, T.; Aittala, M.; Laine, S.; Härkönen, E.; Hellsten, J.; Lehtinen, J.; and Aila, T. 2021 · 2021
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Image Synthesis from Layout with Locality-Aware Mask Adaption
Li, Z.; Wu, J.; Koh, I.; Tang, Y.; and Sun, L. 2021 · 2021
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Improved denoising diffusion probabilistic models
Nichol, A. Q.; and Dhariwal, P. 2021 · 2021
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Dual contradistinctive generative autoencoder
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Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
PyTorch Lightning
Falcon, W.; and team, T. P. L. 2019 · 2019
Cited alongside, same era.
Layoutvae: Stochastic scene layout generation from a label set
Jyothi, A. A.; Durand, T.; He, J.; Sigal, L.; and Mori, G. 2019 · 2019
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T.; Laine, S.; and Aila, T. 2019 · 2019
Cited alongside, same era.
Improved precision and recall metric for assessing generative models
Kynkäänniemi, T.; Karras, T.; Laine, S.; Lehtinen, J.; and Aila, T. 2019 · 2019
Cited alongside, same era.
Object-driven text-to-image synthesis via adversarial training
Li, W.; Zhang, P.; Zhang, L.; Huang, Q.; He, X.; Lyu, S.; and Gao, J. 2019 · 2019
Cited alongside, same era.
Semantic image synthesis with spatially-adaptive normalization
Park, T.; Liu, M.-Y.; Wang, T.-C.; and Zhu, J.-Y. 2019 · 2019
Cited alongside, same era.
Parmar, G.; Li, D.; Lee, K.; and Tu, Z. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
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Projected gans converge faster
Sauer, A.; Chitta, K.; Müller, J.; and Geiger, A. 2021 · 2021
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Aligning latent and image spaces to connect the unconnectable
Skorokhodov, I.; Sotnikov, G.; and Elhoseiny, M. 2021 · 2021
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Object-centric image generation from layouts
Sylvain, T.; Zhang, P.; Bengio, Y.; Hjelm, R. D.; and Sharma, S. 2021 · 2021
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Score-based generative modeling in latent space
Vahdat, A.; Kreis, K.; and Kautz, J. 2021 · 2021
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Videogpt: Video generation using vq-vae and transformers
Yan, W.; Zhang, Y.; Abbeel, P.; and Srinivas, A. 2021 · 2021
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Layouttransformer: Scene layout generation with conceptual and spatial diversity
Yang, C.-F.; Fan, W.-C.; Yang, F.-E.; and Wang, Y.-C. F. 2021 · 2021
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Cross-modal contrastive learning for text-to-image generation
Zhang, H.; Koh, J. Y.; Baldridge, J.; Lee, H.; and Yang, Y. 2021 · 2021
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Maskgit: Masked generative image transformer
Chang, H.; Zhang, H.; Jiang, L.; Liu, C.; and Freeman, W. T. 2022 · 2022
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Make-a-scene: Scene-based text-to-image generation with human priors
Gafni, O.; Polyak, A.; Ashual, O.; Sheynin, S.; Parikh, D.; and Taigman, Y. 2022 · 2022
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Vector quantized diffusion model for text-to-image synthesis
Gu, S.; Chen, D.; Bao, J.; Wen, F.; Zhang, B.; Chen, D.; Yuan, L.; and Guo, B. 2022 · 2022
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Cascaded Diffusion Models for High Fidelity Image Generation
Ho, J.; Saharia, C.; Chan, W.; Fleet, D. J.; Norouzi, M.; and Salimans, T. 2022 · 2022
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Soft truncation: A universal training technique of score-based diffusion model for high precision score estimation
Kim, D.; Shin, S.; Song, K.; Kang, W.; and Moon, I.-C. 2022 · 2022
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Text to image generation with semantic-spatial aware GAN
Liao, W.; Hu, K.; Yang, M. Y.; and Rosenhahn, B. 2022 · 2022
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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. 2022 · 2022
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DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents
Pandey, K.; Mukherjee, A.; Rai, P.; and Kumar, A. 2022 · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A.; Dhariwal, P.; Nichol, A.; Chu, C.; and Chen, M. 2022 · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
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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 · 2022
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Improved Vector Quantized Diffusion Models
Tang, Z.; Gu, S.; Bao, J.; Chen, D.; and Wen, F. 2022 · 2022
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DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesis
Tao, M.; Tang, H.; Wu, F.; Jing, X.-Y.; Bao, B.-K.; and Xu, C. 2022 · 2022
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Modeling Image Composition for Complex Scene Generation
Yang, Z.; Liu, D.; Wang, C.; Yang, J.; and Tao, D. 2022 · 2022
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Vector-quantized image modeling with improved vqgan
Yu, J.; Li, X.; Koh, J. Y.; Zhang, H.; Pang, R.; Qin, J.; Ku, A.; Xu, Y.; Baldridge, J.; and Wu, Y. 2022 · 2022
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LAFITE: Towards Language-Free Training for Text-to-Image Generation
Zhou, Y.; Zhang, R.; Chen, C.; Li, C.; Tensmeyer, C.; Yu, T.; Gu, J.; Xu, J.; and Sun, T. 2022 · 2022
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