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During image editing, existing deep generative models tend to re-synthesize the entire output from scratch, including the unedited regions.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” NeurIPS , 2014
2014
Earlier work this paper cites.
M. Jaderberg, A. Vedaldi, and A. Zisserman, “Speeding up convolutional neural networks with low rank expansions,” in BMVC , 2014
2014
Earlier work this paper cites.
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in ICML , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” NeurIPS , 2015
2015
Earlier work this paper cites.
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Pensky, “Sparse convolutional neural networks,” in CVPR , 2015
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in ICML , 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18 . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Z. Liu, P. Luo, X. Wang, and X. Tang, “Deep learning face attributes in the wild,” in ICCV , 2015
2015
Earlier work this paper cites.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in CVPR , 2016
2016
Earlier work this paper cites.
J.-Y. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros, “Generative visual manipulation on the natural image manifold,” in ECCV , 2016
2016
Earlier work this paper cites.
S. Han, H. Mao, and W. J. Dally, “Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding,” in ICLR , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “Xnor-net: Imagenet classification using binary convolutional neural networks,” in ECCV , 2016
2016
Earlier work this paper cites.
H. Li, A. Kadav, I. Durdanovic, H. Samet, and H. P. Graf, “Pruning filters for efficient convnets,” ICLR , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in CVPR , 2016
2016
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 CVPR , 2017
2017
Earlier work this paper cites.
P. Sangkloy, J. Lu, C. Fang, F. Yu, and J. Hays, “Scribbler: Controlling deep image synthesis with sketch and color,” in CVPR , 2017
2017
Earlier work this paper cites.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in ICCV , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Lin, Y. Rao, J. Lu, and J. Zhou, “Runtime neural pruning,” in NeurIPS , 2017
2017
Earlier work this paper cites.
Y. He, X. Zhang, and J. Sun, “Channel pruning for accelerating very deep neural networks,” in ICCV , 2017
2017
Earlier work this paper cites.
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang, “Learning efficient convolutional networks through network slimming,” in ICCV , 2017
2017
Earlier work this paper cites.
B. Zoph and Q. V. Le, “Neural Architecture Search with Reinforcement Learning,” in ICLR , 2017
2017
Earlier work this paper cites.
G. Riegler, A. Osman Ulusoy, and A. Geiger, “Octnet: Learning deep 3d representations at high resolutions,” in CVPR , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
X. Dong, J. Huang, Y. Yang, and S. Yan, “More is less: A more complicated network with less inference complexity,” in CVPR , 2017
2017
Earlier work this paper cites.
X. Li, Z. Liu, P. Luo, C. Change Loy, and X. Tang, “Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade,” in CVPR , 2017
2017
Earlier work this paper cites.
X. Huang and S. Belongie, “Arbitrary style transfer in real-time with adaptive instance normalization,” in ICCV , 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” NeurIPS , 2017
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,” NeurIPS , 2017
2017
Earlier work this paper cites.
F. Yu, V. Koltun, and T. Funkhouser, “Dilated residual networks,” in CVPR , 2017
2017
Earlier work this paper cites.
M. Ren, A. Pokrovsky, B. Yang, and R. Urtasun, “Sbnet: Sparse blocks network for fast inference,” in CVPR , 2018
2018
Earlier work this paper cites.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in CVPR , 2018
2018
Earlier work this paper cites.
Y. He, J. Lin, Z. Liu, H. Wang, L.-J. Li, and S. Han, “AMC: AutoML for Model Compression and Acceleration on Mobile Devices,” in ECCV , 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, “Quantization and training of neural networks for efficient integer-arithmetic-only inference,” in CVPR , 2018
2018
Cited alongside, same era.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition,” in CVPR , 2018
2018
Cited alongside, same era.
B. Pan, W. Lin, X. Fang, C. Huang, B. Zhou, and C. Lu, “Recurrent residual module for fast inference in videos,” in CVPR , 2018
L. Hou, Z. Yuan, L. Huang, H. Shen, X. Cheng, and C. Wang, “Slimmable generative adversarial networks,” in AAAI , 2021
2021
Later among the works it cites.
Q. Jin, J. Ren, O. J. Woodford, J. Wang, G. Yuan, Y. Wang, and S. Tulyakov, “Teachers do more than teach: Compressing image-to-image models,” in CVPR , 2021
2021
Later among the works it cites.
T. R. Shaham, M. Gharbi, R. Zhang, E. Shechtman, and T. Michaeli, “Spatially-adaptive pixelwise networks for fast image translation,” in CVPR , 2021
2021
Later among the works it cites.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” NeurIPS , 2021
2021
Later among the works it cites.
P. Esser, R. Rombach, and B. Ommer, “Taming transformers for high-resolution image synthesis,” in CVPR , 2021
2021
Later among the works it cites.
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2018
Cited alongside, same era.
Y. Wu and K. He, “Group normalization,” in ECCV , 2018
2018
Cited alongside, same era.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in CVPR , 2018
2018
Cited alongside, same era.
T. Park, M.-Y. Liu, T.-C. Wang, and J.-Y. Zhu, “Semantic image synthesis with spatially-adaptive normalization,” in CVPR , 2019
2019
Cited alongside, same era.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in CVPR , 2019
2019
Cited alongside, same era.
A. Brock, J. Donahue, and K. Simonyan, “Large scale gan training for high fidelity natural image synthesis,” in ICLR , 2019
2019
Cited alongside, same era.
A. Razavi, A. Van den Oord, and O. Vinyals, “Generating diverse high-fidelity images with vq-vae-2,” in NeurIPS , vol. 32, 2019
2019
Cited alongside, same era.
R. Abdal, Y. Qin, and P. Wonka, “Image2stylegan: How to embed images into the stylegan latent space?” in ICCV , 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
J. Choi, S. Kim, Y. Jeong, Y. Gwon, and S. Yoon, “Ilvr: Conditioning method for denoising diffusion probabilistic models,” in ICCV , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
O. Patashnik, Z. Wu, E. Shechtman, D. Cohen-Or, and D. Lischinski, “Styleclip: Text-driven manipulation of stylegan imagery,” in ICCV , 2021
2021
Later among the works it cites.
Z. Kong and W. Ping, “On fast sampling of diffusion probabilistic models,” in ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models , 2021
2021
Later among the works it cites.
J. Lin, R. Zhang, F. Ganz, S. Han, and J.-Y. Zhu, “Anycost gans for interactive image synthesis and editing,” in CVPR , 2021
2021
Later among the works it cites.
Y. Liu, Z. Shu, Y. Li, Z. Lin, F. Perazzi, and S.-Y. Kung, “Content-aware gan compression,” in CVPR , 2021
2021
Later among the works it cites.
R. Ma and J. Lou, “Cpgan: An efficient architecture designing for text-to-image generative adversarial networks based on canonical polyadic decomposition,” Scientific Programming , 2021
2021
Later among the works it cites.
L. Wang, X. Dong, Y. Wang, X. Ying, Z. Lin, W. An, and Y. Guo, “Exploring sparsity in image super-resolution for efficient inference,” in CVPR , 2021
2021
Later among the works it cites.
Y. Han, G. Huang, S. Song, L. Yang, Y. Zhang, and H. Jiang, “Spatially adaptive feature refinement for efficient inference,” TIP , 2021
2021
Later among the works it cites.
A. Q. Nichol and P. Dhariwal, “Improved denoising diffusion probabilistic models,” in ICML , 2021
2021
Later among the works it cites.
Y. Ding, L. Zhu, Z. Jia, G. Pekhimenko, and S. Han, “Ios: Inter-operator scheduler for cnn acceleration,” MLSys , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
S. Zhao, J. Cui, Y. Sheng, Y. Dong, X. Liang, E. I. Chang, and Y. Xu, “Large scale image completion via co-modulated generative adversarial networks,” in ICLR , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in CVPR , 2022
2022
Closest in time.
C. Meng, Y. He, Y. Song, J. Song, J. Wu, J.-Y. Zhu, and S. Ermon, “SDEdit: Guided image synthesis and editing with stochastic differential equations,” in ICLR , 2022
2022
Closest in time.
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman et al. , “Laion-5b: An open large-scale dataset for training next generation image-text models,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 278–25 294, 2022
2022
Closest in time.
S. Li, M. Lin, Y. Wang, C. Fei, L. Shao, and R. Ji, “Learning efficient gans for image translation via differentiable masks and co-attention distillation,” IEEE Transactions on Multimedia , 2022
2022
Closest in time.
M. Li, J. Lin, C. Meng, S. Ermon, S. Han, and J.-Y. Zhu, “Efficient spatially sparse inference for conditional gans and diffusion models,” in NeurIPS , 2022
2022
Closest in time.
C. Saharia, W. Chan, H. Chang, C. Lee, J. Ho, T. Salimans, D. Fleet, and M. Norouzi, “Palette: Image-to-image diffusion models,” in SIGGRAPH , 2022
2022
Closest in time.
Z. Xiao, K. Kreis, and A. Vahdat, “Tackling the generative learning trilemma with denoising diffusion GANs,” in ICLR , 2022
2022
Closest in time.
H. Tang, Z. Liu, X. Li, Y. Lin, and S. Han, “Torchsparse: Efficient point cloud inference engine,” in MLSys , 2022
2022
Closest in time.
Y. Wang, Y. Yue, Y. Lin, H. Jiang, Z. Lai, V. Kulikov, N. Orlov, H. Shi, and G. Huang, “Adafocus v2: End-to-end training of spatial dynamic networks for video recognition,” in CVPR , 2022
2022
Closest in time.
M. Parger, C. Tang, C. D. Twigg, C. Keskin, R. Wang, and M. Steinberger, “Deltacnn: End-to-end cnn inference of sparse frame differences in videos,” in CVPR , 2022
2022
Closest in time.
G. Parmar, R. Zhang, and J.-Y. Zhu, “On aliased resizing and surprising subtleties in gan evaluation,” in CVPR , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.