{"type":"ScholarlyArticle","title":"Bring Metric Functions into Diffusion Models","authors":[{"name":"Jie An","slug":"jie-an","given":"Jie","family":"An","affiliation":"University of Rochester"},{"name":"Zhengyuan Yang","slug":"zhengyuan-yang","given":"Zhengyuan","family":"Yang","affiliation":"Microsoft"},{"name":"Jianfeng Wang","slug":"jianfeng-wang","given":"Jianfeng","family":"Wang","affiliation":"Microsoft"},{"name":"Linjie Li","slug":"linjie-li","given":"Linjie","family":"Li","affiliation":"Microsoft"},{"name":"Zicheng Liu","slug":"zicheng-liu","given":"Zicheng","family":"Liu","affiliation":"Microsoft"},{"name":"Lijuan Wang","slug":"lijuan-wang","given":"Lijuan","family":"Wang","affiliation":"Microsoft"},{"name":"Jiebo Luo","slug":"jiebo-luo","given":"Jiebo","family":"Luo","affiliation":"University of Rochester"}],"abstract":"We introduce a Cascaded Diffusion Model (Cas-DM) that improves a Denoising Diffusion Probabilistic Model (DDPM) by effectively incorporating additional metric functions in training. Metric functions such as the LPIPS loss have been proven highly effective in consistency models derived from the score matching. However, for the diffusion counterparts, the methodology and efficacy of adding extra metric functions remain unclear. One major challenge is the mismatch between the noise predicted by a DDPM at each step and the desired clean image that the metric function works well on. To address this problem, we propose Cas-DM, a network architecture that cascades two network modules to effectively apply metric functions to the diffusion model training. The first module, similar to a standard DDPM, learns to predict the added noise and is unaffected by the metric function. The second cascaded module learns to predict the clean image, thereby facilitating the metric function computation. Experiment results show that the proposed diffusion model backbone enables the effective use of the LPIPS loss, improving the image quality (FID, sFID) of diffusion models on various established benchmarks.","doi":"10.24963/ijcai.2024/64","url":"https://ijcai.org/proceedings/2024/64","pdf":"https://ijcai.org/proceedings/2024/0064.pdf","markdown":"https://ijcai.org/proceedings/2024/64.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/64","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":64,"pages":{"first":578,"last":586},"keywords":["Computer Vision: CV: Image and video synthesis and generation"],"videos":[],"references":[{"position":1,"raw":"[An et al., 2023] Jie An, Songyang Zhang, Harry Yang, Sonal Gupta, Jia-Bin Huang, Jiebo Luo, and Xi Yin. Latent-shift: Latent diffusion with temporal shift for efficient text-to-video generation. arXiv preprint arXiv:2304.08477, 2023.","title":"Latent-shift: Latent diffusion with temporal shift for efficient text-to-video generation","authors":"Jie An, Songyang Zhang, Harry Yang, Sonal Gupta, Jia-Bin Huang, Jiebo Luo, Xi Yin","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bengio et al., 2014] Yoshua Bengio, Eric Laufer, Guil- laume Alain, and Jason Yosinski. Deep generative stochas- tic networks trainable by backprop. In International Con- ference on Machine Learning, pages 226-234. PMLR, 2014.","title":"Deep generative stochastic networks trainable by backprop","authors":"Yoshua Bengio, Eric Laufer, Guillaume Alain, Jason Yosinski","year":"2014","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Benny and Wolf, 2022] Yaniv Benny and Lior Wolf. Dy- namic dual-output diffusion models. In CVPR, 2022.","title":"Dynamic dual-output diffusion models","authors":"Yaniv Benny, Lior Wolf","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Blattmann et al., 2023] Andreas Blattmann, Robin Rom- bach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, and Karsten Kreis. Align your latents: High- resolution video synthesis with latent diffusion models. In CVPR, 2023.","title":"Align your latents: Highresolution video synthesis with latent diffusion models","authors":"Andreas Blattmann, Robin Rombach, Huan Ling, Tim Dockhorn, Seung Wook Kim, Sanja Fidler, Karsten Kreis","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Brock et al., 2018] Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018.","title":"Large scale gan training for high fidelity natural image synthesis","authors":"Andrew Brock, Jeff Donahue, Karen Simonyan","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Che et al., 2020] Tong Che, Ruixiang Zhang, Jascha Sohl- Dickstein, Hugo Larochelle, Liam Paull, Yuan Cao, and Yoshua Bengio. Your gan is secretly an energy-based model and you should use discriminator driven latent sam- pling. NeurIPS, 2020.","title":"Your gan is secretly an energy-based model and you should use discriminator driven latent sampling","authors":"Ruixiang Tong Che, Jascha Zhang, Hugo Sohl-Dickstein, Liam Larochelle, Yuan Paull, Yoshua Cao, Bengio","year":"2020","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2018] Xi Chen, Nikhil Mishra, Mostafa Roha- ninejad, and Pieter Abbeel. Pixelsnail: An improved au- toregressive generative model. In ICML, 2018.","title":"Pixelsnail: An improved autoregressive generative model","authors":"Xi Chen, Nikhil Mishra, Mostafa Rohaninejad, Pieter Abbeel","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Deng et al., 2009] Jia Deng, Wei Dong, Richard Socher, Li- Jia Li, Kai Li, and Li Fei-Fei. Imagenet: a large-scale hierarchical image database. In CVPR, 2009.","title":"Imagenet: a large-scale hierarchical image database","authors":"Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei","year":"2009","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Dhariwal and Nichol, 2021] Prafulla Dhariwal and Alexan- der Nichol. Diffusion models beat gans on image synthe- sis. In NeurIPS, 2021.","title":"Diffusion models beat gans on image synthesis","authors":"Nichol Dhariwal","year":"2021","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Dinh et al., 2014] Laurent Dinh, David Krueger, and Yoshua Bengio. Nice: Non-linear independent compo- nents estimation. arXiv preprint arXiv:1410.8516, 2014.","title":"Nice: Non-linear independent components estimation","authors":"Laurent Dinh, David Krueger, Yoshua Bengio","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Dinh et al., 2017] Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio. Density estimation using real nvp. In ICLR, 2017.","title":"Density estimation using real nvp","authors":"Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio","year":"2017","venue":"ICLR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Esser et al., 2021] Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In CVPR, 2021.","title":"Taming transformers for high-resolution image synthesis","authors":"Patrick Esser, Robin Rombach, Bjorn Ommer","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Fan et al., 2023] Wan-Cyuan Fan, Yen-Chun Chen, Dong- Dong Chen, Yu Cheng, Lu Yuan, and Yu-Chiang Frank Wang. Frido: Feature pyramid diffusion for complex scene image synthesis. In AAAI, 2023.","title":"Frido: Feature pyramid diffusion for complex scene image synthesis","authors":"Wan-Cyuan Fan, Yen-Chun Chen, Dong-Dong Chen, Yu Cheng, Lu Yuan, Yu-Chiang Frank, Wang","year":"2023","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Gen- erative adversarial nets. In NeurIPS, 2014. [He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In CVPR, 2016.","title":"Deep residual learning for image recognition","authors":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio, ; Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2014","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Heusel et al., 2017] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochre- iter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS, 2017.","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","authors":"Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, Sepp Hochreiter","year":"2017","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ho et al., 2019] Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, and Pieter Abbeel. Flow++: Improving flow- based generative models with variational dequantization and architecture design. In ICML, 2019.","title":"Flow++: Improving flowbased generative models with variational dequantization and architecture design","authors":"Jonathan Ho, Xi Chen, Aravind Srinivas, Yan Duan, Pieter Abbeel","year":"2019","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ho et al., 2020] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. NeurIPS, 2020.","title":"Denoising diffusion probabilistic models","authors":"Jonathan Ho, Ajay Jain, Pieter Abbeel","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ho et al., 2022] Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik P Kingma, Ben Poole, Mohammad Norouzi, David J Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303, 2022.","title":"Imagen video: High definition video generation with diffusion models","authors":"Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, P Diederik, Ben Kingma, Mohammad Poole, David J Norouzi, Fleet","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Johnson et al., 2016] Justin Johnson, Alexandre Alahi, and Li Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016.","title":"Perceptual losses for real-time style transfer and super-resolution","authors":"Justin Johnson, Alexandre Alahi, Li Fei-Fei","year":"2016","venue":"ECCV","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Jolicoeur-Martineau et al., 2020] Alexia Jolicoeur- Martineau, Rémi Piché-Taillefer, Rémi Tachet des Combes, and Ioannis Mitliagkas. Adversarial score matching and improved sampling for image generation. arXiv preprint arXiv:2009.05475, 2020.","title":"Adversarial score matching and improved sampling for image generation","authors":"Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Rémi Tachet Des Combes, Ioannis Mitliagkas","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Karras et al., 2017] Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196, 2017.","title":"Progressive growing of gans for improved quality, stability, and variation","authors":"Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Karras et al., 2019] Tero Karras, Samuli Laine, and Timo Aila. A style-based generator architecture for generative adversarial networks. In CVPR, 2019.","title":"A style-based generator architecture for generative adversarial networks","authors":"Tero Karras, Samuli Laine, Timo Aila","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Karras et al., 2020] Tero Karras, Samuli Laine, Miika Ait- tala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. An- alyzing and improving the image quality of stylegan. In CVPR, 2020.","title":"Analyzing and improving the image quality of stylegan","authors":"Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, Timo Aila","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Kingma and Dhariwal, 2018] Durk P Kingma and Prafulla Dhariwal. Glow: Generative flow with invertible 1x1 con- volutions. In NeurIPS, 2018.","title":"Glow: Generative flow with invertible 1x1 convolutions","authors":"P Durk, Prafulla Kingma, Dhariwal","year":"2018","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Kingma and Welling, 2013] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.","title":"Auto-encoding variational bayes","authors":"P Diederik, Max Kingma, Welling","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Krizhevsky et al., 2009] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny im- ages. 2009.","title":"Learning multiple layers of features from tiny images","authors":"Alex Krizhevsky, Geoffrey Hinton","year":"2009","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Liu et al., 2021] Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In ICCV, 2021.","title":"Swin transformer: Hierarchical vision transformer using shifted windows","authors":"Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo","year":"2021","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Mao et al., 2017] Xudong Mao, Qing Li, Haoran Xie, Ray- mond YK Lau, Zhen Wang, and Stephen Paul Smolley. Least squares generative adversarial networks. In ICCV, 2017.","title":"Least squares generative adversarial networks","authors":"Xudong Mao, Qing Li, Haoran Xie, Raymond Yk Lau, Zhen Wang, Stephen Paul Smolley","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Nash et al., 2021] Charlie Nash, Jacob Menick, Sander Dieleman, and Peter W Battaglia. Generating images with sparse representations. arXiv preprint arXiv:2103.03841, 2021.","title":"Generating images with sparse representations","authors":"Charlie Nash, Jacob Menick, Sander Dieleman, Peter W Battaglia","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Nichol and Dhariwal, 2021] Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion proba- bilistic models. In ICML, 2021.","title":"Improved denoising diffusion probabilistic models","authors":"Nichol, Dhariwal, Alexander Quinn, Nichol, Prafulla Dhariwal","year":"2021","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Oord et al., 2016] Aaron van den Oord, Sander Diele- man, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu. Wavenet: A generative model for raw au- dio. arXiv preprint arXiv:1609.03499, 2016.","title":"Wavenet: A generative model for raw audio","authors":"Oord","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Ramesh et al., 2022] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 2022.","title":"Hierarchical text-conditional image generation with clip latents","authors":"Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, Mark Chen","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Razavi et al., 2019] Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. NeurIPS, 2019.","title":"Generating diverse high-fidelity images with vq-vae-2","authors":"Ali Razavi, Aaron Van Den Oord, Oriol Vinyals","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Rombach et al., 2022] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In CVPR, 2022.","title":"High-resolution image synthesis with latent diffusion models","authors":"Rombach","year":"2022","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Saharia et al., 2022a] Chitwan Saharia, William Chan, Hui- wen Chang, Chris Lee, Jonathan Ho, Tim Salimans, David Fleet, and Mohammad Norouzi. Palette: Image-to-image diffusion models. In SIGGRAPH, 2022.","title":"Palette: Image-to-image diffusion models","authors":"Saharia","year":"2022","venue":"SIGGRAPH","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Saharia et al., 2022b] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Den- ton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to- image diffusion models with deep language understand- ing. NeurIPS, 2022.","title":"Photorealistic text-toimage diffusion models with deep language understanding","authors":"Saharia","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Salimans et al., 2015] Tim Salimans, Diederik Kingma, and Max Welling. Markov chain monte carlo and variational inference: Bridging the gap. In ICML, 2015.","title":"Markov chain monte carlo and variational inference: Bridging the gap","authors":"Tim Salimans, Diederik Kingma, Max Welling","year":"2015","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Salimans et al., 2017] Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma. Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications. arXiv preprint arXiv:1701.05517, 2017.","title":"Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications","authors":"Tim Salimans, Andrej Karpathy, Xi Chen, Diederik P Kingma","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Simonyan and Zisserman, 2014] Karen Simonyan and An- drew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.","title":"Very deep convolutional networks for large-scale image recognition","authors":"Karen Simonyan, Andrew Zisserman","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Singer et al., 2022] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni, et al. Make-a- video: Text-to-video generation without text-video data. arXiv preprint arXiv:2209.14792, 2022.","title":"Make-avideo: Text-to-video generation without text-video data","authors":"Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang, Oron Ashual, Oran Gafni","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Song and Ermon, 2019] Yang Song and Stefano Ermon. Generative modeling by estimating gradients of the data distribution. arXiv preprint arXiv:1907.05600, 2019.","title":"Generative modeling by estimating gradients of the data distribution","authors":"Yang Song, Stefano Ermon","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Song and Ermon, 2020] Yang Song and Stefano Ermon. Improved techniques for training score-based generative models. arXiv preprint arXiv:2006.09011, 2020.","title":"Improved techniques for training score-based generative models","authors":"Ermon ; Song, Yang Song, Stefano Ermon","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":43,"raw":"[Song and Kingma, 2021] Yang Song and Diederik P Kingma. How to train your energy-based models. arXiv preprint arXiv:2101.03288, 2021.","title":"How to train your energy-based models","authors":"Yang Song, P Diederik, Kingma","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":44,"raw":"[Song et al., 2020a] Jiaming Song, Chenlin Meng, and Ste- fano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020.","title":"Jiaming Song, Chenlin Meng, and Stefano Ermon","authors":"Song","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":45,"raw":"[Song et al., 2020b] Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Er- mon, and Ben Poole. Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456, 2020.","title":"Score-based generative modeling through stochastic differential equations","authors":"Song","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":46,"raw":"[Song et al., 2023] Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models. 2023.","title":"Consistency models","authors":"Yang Song, Prafulla Dhariwal, Mark Chen, Ilya Sutskever","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":47,"raw":"[Szegedy et al., 2016] Christian Szegedy, Vincent Van- houcke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In CVPR, 2016.","title":"Rethinking the inception architecture for computer vision","authors":"Szegedy","year":"2016","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":48,"raw":"[Vahdat and Kautz, 2020] Arash Vahdat and Jan Kautz. Nvae: A deep hierarchical variational autoencoder. NeurIPS, 2020.","title":"Nvae: A deep hierarchical variational autoencoder","authors":"Kautz Vahdat, Arash Vahdat, Jan Kautz","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":49,"raw":"[Van den Oord et al., 2016a] Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al. Conditional image generation with pixelcnn decoders. NeurIPS, 2016.","title":"Conditional image generation with pixelcnn decoders","authors":"Oord Van Den","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":50,"raw":"[Van Den Oord et al., 2016b] Aäron Van Den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks. In ICML, 2016.","title":"Nal Kalchbrenner, and Koray Kavukcuoglu. Pixel recurrent neural networks","authors":"Oord Van Den","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":51,"raw":"[Van Den Oord et al., 2017] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. NeurIPS, 2017.","title":"Neural discrete representation learning","authors":"Oord Van Den","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":52,"raw":"[Wu et al., 2019] Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, and Timothy Lillicrap. Logan: Latent optimisation for generative adversarial networks. arXiv preprint arXiv:1912.00953, 2019.","title":"Logan: Latent optimisation for generative adversarial networks","authors":"Yan Wu, Jeff Donahue, David Balduzzi, Karen Simonyan, Timothy Lillicrap","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":53,"raw":"[Yang et al., 2023] Zhengyuan Yang, Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, et al. Reco: Region- controlled text-to-image generation. In CVPR, 2023.","title":"Reco: Regioncontrolled text-to-image generation","authors":"Zhengyuan Yang, Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng","year":"2023","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":54,"raw":"[Yu et al., 2015] Fisher Yu, Ari Seff, Yinda Zhang, Shu- ran Song, Thomas Funkhouser, and Jianxiong Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365, 2015.","title":"Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop","authors":"Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, Jianxiong Xiao","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":55,"raw":"[Zhang et al., 2018] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018.","title":"The unreasonable effectiveness of deep features as a perceptual metric","authors":"Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, Oliver Wang","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":56,"raw":"[Zhou et al., 2022] Daquan Zhou, Weimin Wang, Hanshu Yan, Weiwei Lv, Yizhe Zhu, and Jiashi Feng. Magicvideo: Efficient video generation with latent diffusion models. arXiv preprint arXiv:2211.11018, 2022.","title":"Magicvideo: Efficient video generation with latent diffusion models","authors":"Daquan Zhou, Weimin Wang, Hanshu Yan, Weiwei Lv, Yizhe Zhu, Jiashi Feng","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}