The DOI matches its Crossref record; 44 of the 46 references it cites were found in a registry or an index.
Learning to generate images with perceptual similarity metrics
Open accessAbstract
Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compact representation. Supervised training of image-synthesis networks typically uses a pixel-wise loss (PL) to indicate the mismatch between a generated image and its corresponding target image. We propose instead to use a loss function that is better calibrated to human perceptual judgments of image quality: the multiscale structural-similarity score (MS-SSIM) [1]. Because MS-SSIM is differentiable, it is easily incorporated into gradient-descent learning. We compare the consequences of using MS-SSIM versus PL loss on training autoencoders. Human observers reliably prefer images synthesized by MS-SSIM-optimized models over those synthesized by PL-optimized models, for two distinct PL measures (L1and L2distances). We also explore the effect of training objective on image encoding and analyze conditions under which perceptually-optimized representations yield better performance on image classification. Finally, we demonstrate the superiority of perceptually-optimized networks for super-resolution imaging. We argue that significant advances can be made in modeling images through the use of training objectives that are well aligned to characteristics of human perception.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Conversely, modern deep-learning metrics like LPIPS offer better perceptual alignment but remain computationally prohibitive for real-time, resource-constrained environments.”
From A Survey and Tutorial on Image Quality Assessment with a Contrast-Weighted Structural Similarity Framework (2026) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep learning-based similarity [29-34]: Semantic similarity learning uses convolutional neural networks (CNNs) to embed images in a structured feature space. Metrics such as LPIPS (learned perceptual image patch similarity) correlate strongly with human judgment but incur high computational costs, offer low interpretability (a 'black-box' nature), and are vulnerable to adversarial perturbations.”
From A Survey and Tutorial on Image Quality Assessment with a Contrast-Weighted Structural Similarity Framework (2026) · cites this paper as [30]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Image Super-Resolution Using Deep Convolutional Networks
Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105
Martin, D., Fowlkes, C., Tal, D., & Malik, J. (2002) · Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 · doi:10.1109/iccv.2001.937655 · Cited by 8,216
Research works printed but not linked: 1
- 10.1016/0364-0213(92)90036-T · DOI · Paper page
Cited by
Sorted by citations
Autoencoding beyond pixels using a learned similarity metric
Larsen, A., Sønderby, S. K., Larochelle, H., & Winther, O. (2016) · Research at the University of Copenhagen (University of Copenhagen) · Cited by 1,317
Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser
Liao, F., Liang, M., Dong, Y., Pang, T., Hu, X., & Zhu, J. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00191 · Cited by 987
Pros and cons of GAN evaluation measures
Borji, A. (2019) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2018.10.009 · Cited by 963
High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis
Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., & Li, H. (2017) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2017.434 · Cited by 894
Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
Bergmann, P., Löwe, S., Fauser, M., Sattlegger, D., & Steger, C. T. (2019) · International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) · doi:10.5220/0007364503720380 · Cited by 893
Video Frame Interpolation via Adaptive Separable Convolution
Niklaus, S., Mai, L., & Liu, F. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.37 · Cited by 746
Autoencoding beyond pixels using a learned similarity metricPreprint
Larsen, A., Sønderby, S. K., Larochelle, H., & Winther, O. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.09300 · Cited by 813
Deep Feature Consistent Variational Autoencoder
Hou, X., Shen, L., Sun, K., & Qiu, G. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.131 · Cited by 374
Singla, A., Padakandla, S., & Bhatnagar, S. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2954952 · Cited by 325
Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems
Ding, K., Ma, K., Wang, S., & Simoncelli, E. P. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01419-7 · Cited by 251
Deep Learning-Based Video Coding: A Review and a Case Study
Liu, D., Li, Y., Lin, J., Li, H., & Wu, F. (2021) · ACM Computing Surveys · doi:10.1145/3368405 · Cited by 192
Generative adversarial networks for reconstructing natural images from brain activity
Seeliger, K., Güçlü, U., Ambrogioni, L., Güçlütürk, Y., & van Gerven, M. A. J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.07.043 · Cited by 167
CRRN: Multi-scale Guided Concurrent Reflection Removal Network
Wan, R., Shi, B., Duan, L., Tan, A., & Kot, A. C. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00502 · Cited by 137
Learning Temporal Transformations from Time-Lapse Videos
Zhou, Y., & Berg, T. L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46484-8_16 · Cited by 137
Deep Architectures for Image Compression: A Critical Review
Mishra, D., Singh, S. K., & Singh, R. K. (2021) · Signal Processing · doi:10.1016/j.sigpro.2021.108346 · Cited by 117
Collin, A.-S., & De Vleeschouwer, C. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9412842 · Cited by 106
Toward Effective Intrusion Detection Using Log-Cosh Conditional Variational Autoencoder
Xu, X., Li, J., Yang, Y., & Shen, F. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3034621 · Cited by 103
CoRRN: Cooperative Reflection Removal Network
Wan, R., Shi, B., Li, H., Duan, L., Tan, A., & Kot, A. C. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2921574 · Cited by 101
UAV Localization Using Autoencoded Satellite Images
Bianchi, M., & Barfoot, T. D. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3060397 · Cited by 99
Transformable Bottleneck Networks
Olszewski, K., Tulyakov, S., Woodford, O. J., Li, H., & Luo, L. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00774 · Cited by 80
Modeling and Transforming Speech Using Variational Autoencoders
Blaauw, M., & Bonada, J. (2016) · Interspeech (USB) · doi:10.21437/interspeech.2016-1183 · Cited by 79
SiameseGAN: A Generative Model for Denoising of Spectral Domain Optical Coherence Tomography Images
Kande, N. A., Dakhane, R., Dukkipati, A., & Yalavarthy, P. K. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3024097 · Cited by 76
Deep Learning-Based Inverse Scattering With Structural Similarity Loss Functions
Huang, Y., Song, R., Xu, K., Ye, X., Li, C., & Chen, X. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3030321 · Cited by 75
Fringe Pattern Improvement and Super-Resolution Using Deep Learning in Digital Holography
Ren, Z., So, H. K., & Lam, E. Y. M. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2913853 · Cited by 75
ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression
Chen, L.-H., Bampis, C. G., Li, Z., Norkin, A., & Bovik, A. C. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3036752 · Cited by 71
DC-cycleGAN: Bidirectional CT-to-MR synthesis from unpaired data
Wang, J., Wu, Q. M. J., & Pourpanah, F. (2023) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2023.102249 · Cited by 68
Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos
Sui, X., Ma, K., Yao, Y., & Fang, Y. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3050888 · Cited by 65
Improving variational autoencoder with deep feature consistent and generative adversarial training
Hou, X., Sun, K., Shen, L., & Qiu, G. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.03.013 · Cited by 60
Privacy Protection in Street-View Panoramas Using Depth and Multi-View Imagery
Uittenbogaard, R., Sebastian, C., Vijverberg, J. A., Boom, B., Gavrila, D. M., & de With, P. H. N. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.01083 · Cited by 60
Autoencoders and variational autoencoders in medical image analysis
Ehrhardt, J., & Wilms, M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824349-7.00015-3 · Cited by 60
Laplacian pyramid adversarial network for face completion
Wang, Q., Fan, H., Sun, G., Cong, Y., & Tang, Y. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.020 · Cited by 48
New Ideas and Trends in Deep Multimodal Content Understanding: A Review
Chen, W., Wang, W., Liu, L., & Lew, M. S. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.10.042 · Cited by 45
MimickNet, Mimicking Clinical Image Post- Processing Under Black-Box Constraints
Huang, O., Long, W., Bottenus, N. B., Lerendegui, M., Trahey, G. E., Farsiu, S., & Palmeri, M. L. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2970867 · Cited by 42
On the Evaluation of Conditional GANsPreprint
DeVries, T., Romero, A., Pineda, L., Taylor, G. W., & Drozdzal, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.08175 · Cited by 49
RETRACTED: Perceptual metric-guided human image generation
Wu, H., He, F., Duan, Y., & Yan, X. (2021) · Integrated Computer-Aided Engineering · doi:10.3233/ica-210672 · Cited by 40
RetractedPerson Re-Identification without Identification via Event Anonymization
Ahmad, S., Morerio, P., & Del Bue, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01022 · Cited by 39
Knyaz, V. A., Выголов, О. В., Kniaz, V. V., Vizilter, Y. V., Сергеевич, Г. В., Lühmann, T., & Conen, N. (2017) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2017.252 · Cited by 37
Efficient image denoising with heterogeneous kernel-based CNN
Hu, Y., Tian, C., Zhang, J., & Zhang, S. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127799 · Cited by 34
Fast and slow curiosity for high-level exploration in reinforcement learning
Bougie, N., & Ichise, R. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01849-3 · Cited by 35
Equivariant Neural RenderingPreprint
Dupont, E., Bautista, M. Á., Colburn, A., Sankar, A., Guestrin, C., Susskind, J., & Shan, Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2006.07630 · Cited by 32
Mukherjee, L., Bui, H. D., Keikhosravi, A., Loeffler, A. G., & Eliceiri, K. W. (2019) · Journal of Biomedical Optics · doi:10.1117/1.jbo.24.12.126003 · Cited by 30
Validating Seed Data Samples for Synthetic Identities – Methodology and Uniqueness Metrics
Varkarakis, V., Bazrafkan, S., Costache, G., & Corcoran, P. (2020) · IEEE Access · doi:10.1109/access.2020.3016097 · Cited by 28
Huang, L., Li, Y., Pillar, N., Haran, T. K., Wallace, W. D., & Özcan, A. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01421-9 · Cited by 24
Li, S., Cao, Y., Gdoutos, E. E., Tao, M., Alkayem, N. F., Avcı, O., & Cao, M. (2024) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2024.111653 · Cited by 24
Dynamic PET Image Denoising With Deep Learning-Based Joint Filtering
He, Y., Cao, S., Zhang, H., Sun, H., Wang, F., Zhu, H., Lv, W., & Lu, L. (2021) · IEEE Access · doi:10.1109/access.2021.3064926 · Cited by 24
Wicaksono, K. P., Fujimoto, K., Fushimi, Y., Sakata, A., Okuchi, S., Hinoda, T., Nakajima, S., Yamao, Y., Yoshida, K., Miyake, K. K., et al. (2022) · European Radiology · doi:10.1007/s00330-022-09103-9 · Cited by 23
AI Radar Sensor: Creating Radar Depth Sounder Images Based on Generative Adversarial Network
Rahnemoonfar, M., Johnson, J., & Paden, J. (2019) · Sensors · doi:10.3390/s19245479 · Cited by 23
Quality Assessment of SAR-to-Optical Image Translation
Zhang, J., Zhou, J., Li, M., Zhou, H., & Yu, T. (2020) · Remote Sensing · doi:10.3390/rs12213472 · Cited by 23
A Domain-Guided Noise-Optimization-Based Inversion Method for Facial Image Manipulation
Yang, N., Zheng, Z., Zhou, M., Guo, X., Qi, L., & Wang, T. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3089905 · Cited by 23
Song, R., Huang, Y., Ye, X., Xu, K., Li, C., & Chen, X. (2022) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2021.3139645 · Cited by 22
Martínez-Murcia, F. J., Arco, J. E., Jiménez-Mesa, C., Segovia, F., Illan, I. A., Ramı́rez, J., & Górriz, J. M. (2024) · International Journal of Neural Systems · doi:10.1142/s0129065724500436 · Cited by 25
An attentive-based generative model for medical image synthesis
Wang, J., Wu, Q. M. J., & Pourpanah, F. (2023) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-01871-0 · Cited by 21
Huang, J., Chen, K., Ren, Y., Sun, J., Wang, Y., Tao, T., & Pu, X. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107219 · Cited by 21
Synthesizing human-like sketches from natural images using a conditional convolutional decoder
Kampelmühler, M., & Pinz, A. J. (2020) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv45572.2020.9093440 · Cited by 18
Detection of Anomalous Grapevine Berries Using Variational Autoencoders
Miranda, M., Zabawa, L., Kicherer, A., Strothmann, L., Rascher, U., & Roscher, R. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.729097 · Cited by 18
Generative Smoke RemovalPreprint
Sidorov, O., Wang, C., & Cheikh, F. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.00311 · Cited by 18
Bias Correcting Climate Model Simulations Using Unpaired Image-to-Image Translation Networks
Fulton, D. J., Clarke, B. J., & Hegerl, G. C. (2023) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-22-0031.1 · Cited by 18
Ahmad, S., Morerio, P., & Del Bue, A. (2024) · IEEE Access · doi:10.1109/access.2024.3399539 · Cited by 16
Islam, A., & Belhouari, S. B. (2023) · IEEE Access · doi:10.1109/access.2023.3259236 · Cited by 16
Lagerquist, R., & Ebert‐Uphoff, I. (2022) · Artificial Intelligence for the Earth Systems · doi:10.1175/aies-d-22-0021.1 · Cited by 16
Smart GAN: a smart generative adversarial network for limited imbalanced dataset
Kumari, D., Vyshnavi, S., Dhar, R., Rajita, B. S. A. S., Panda, S., & Christopher, J. J. (2024) · The Journal of Supercomputing · doi:10.1007/s11227-024-06198-3 · Cited by 15
Radar Sensor Simulation with Generative Adversarial Network
Rahnemoonfar, M., Yari, M., & Paden, J. (2020) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss39084.2020.9323676 · Cited by 15
Kancharla, P., & Channappayya, S. S. (2018) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2018.8451296 · Cited by 14
Learning a multiscale patch-based representation for image denoising in X-RAY fluoroscopy
Matviychuk, Y., Mailhé, B., Chen, X., Wang, Q., Kiraly, A. P., Strobel, N. K., & Nadar, M. S. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532775 · Cited by 14
Multi-task generative adversarial network for retinal optical coherence tomography image denoising
Xie, Q., Ma, Z., Zhu, L., Fan, F., Meng, X., Gao, X., & Zhu, J. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac944a · Cited by 13
Guan, S., & Loew, M. H. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06031-5 · Cited by 14
Khojaste-Sarakhsi, M., Haghighi, S. S., Ghomi, S. M. T. F., & Marchiori, E. (2024) · Image and Vision Computing · doi:10.1016/j.imavis.2024.105017 · Cited by 12
Arnold, S., & Yamazaki, K. (2019) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2019.00022 · Cited by 12
CNN-Based Metrics for Performance Evaluation of Generative Adversarial Networks
Behera, A. P., Prakash, S., Khanna, S., Nigam, S., & Verma, S. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3401650 · Cited by 11
Sharma, A., Jindal, N., & Rana, P. S. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09308-4 · Cited by 11
CAGAN: Text-To-Image Generation with Combined Attention Generative Adversarial Networks
Schulze, H., Yaman, D., & Waibel, A. H. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-92659-5_25 · Cited by 11
Revealing True Identity: Detecting Makeup Attacks in Face-based Biometric Systems
Arab, M. A., Moghadam, P. A., Hussein, M. E., AbdAlmageed, W., & Hefeeda, M. M. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413606 · Cited by 11
Face Photo-Sketch Recognition Using Bidirectional Collaborative Synthesis Network
Bae, S., Din, N. U., Park, H., & Yi, J. (2022) · International Conference on Ubiquitous Information Management and Communication (IMCOM) · doi:10.1109/imcom53663.2022.9721719 · Cited by 10
Perceptually Optimizing Deep Image CompressionPreprint
Chen, L., Bampis, C. G., Li, Z., Andrey Norkin, A., & Bovik, A. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.02711 · Cited by 10
A Variational Auto-Encoder Approach for Image Transmission in Noisy Channel
Estiri, A. H., Sabramooz, M. R., Banaei, A., Dehghan, A. H., Jamialahmadi, B., & Siavoshani, M. J. (2020) · International Symposium on Telecommunications · doi:10.1109/ist50524.2020.9345813 · Cited by 9
A Single-Shot Harmonic Imaging Approach Utilizing Deep Learning for Medical Ultrasound
Fouad, M., Ghany, M. A. A. E., & Schmitz, G. (2023) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2023.3234230 · Cited by 9
Touati, R., & Kadoury, S. (2023) · Physics in Medicine and Biology · doi:10.1088/1361-6560/acda78 · Cited by 9
Unsupervised Anomaly Detection in 3D Brain FDG PET: A Benchmark of 17 VAE-Based Approaches
Hassanaly, R., Brianceau, C., Colliot, O., & Burgos, N. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53767-7_11 · Cited by 9
PixRevive: Latent Feature Diffusion Model for Compressed Video Quality Enhancement
Wang, W., Jing, M., Fan, Y., & Weng, W. (2024) · Sensors · doi:10.3390/s24061907 · Cited by 10
Liu, P., Chen, Y., Yu, F., & Zhang, Q. (2024) · The Visual Computer · doi:10.1007/s00371-024-03663-1 · Cited by 9
Generating High-Fidelity Images with Disentangled Adversarial VAEs and Structure-Aware Loss
Naderi, H., Soleimani, B. H., & Matwin, S. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207056 · Cited by 9
Image2GIF: Generating Cinemagraphs Using Recurrent Deep Q-Networks
Zhou, Y., Song, Y., & Berg, T. L. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00025 · Cited by 9
Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use CasePreprint
Chadebec, C., Vincent, L. J., & Allassonnière, S. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2206.08309 · Cited by 9
Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission
Letafati, M., Kalkhoran, S. A. A., Erdemir, E., Khalaj, B. H., Behroozi, H., & Gündüz, D. (2025) · IEEE Transactions on Machine Learning in Communications and Networking · doi:10.1109/tmlcn.2025.3564907 · Cited by 8
Scalable Font Reconstruction with Dual Latent Manifolds
Srivatsan, N., Wu, S., Barron, J. T., & Berg-Kirkpatrick, T. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.244 · Cited by 8
An image interpolation approach for acquisition time reduction in navigator-based 4D MRI
Karani, N., Zhang, L., Tanner, C., & Konukoğlu, E. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.02.008 · Cited by 8
Dynamics of Fourier Modes in Torus Generative Adversarial Networks
González-Prieto, Á., Mozó, A., Talavera, E., & Gómez-Canaval, S. (2021) · Mathematics · doi:10.3390/math9040325 · Cited by 8
Temporal Interpolation via Motion Field PredictionPreprint
Zhang, L., Karani, N., Tanner, C., & Konukoğlu, E. (2018) · arXiv (Cornell University) · doi:10.3929/ethz-b-000322976 · Cited by 8
Moshe, Y. H., Buchsweiler, Y., Teicher, M., & Artzi, M. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29072 · Cited by 7
Adversarially Trained Variational Auto-Encoders With Maximum Mean Discrepancy based Regularization
Sathya, R., Sekar, K. T. A., Ananthi, S., & Dheepa, T. (2022) · International Conference on Knowledge Engineering and Communication Systems (ICKECS) · doi:10.1109/ickecs56523.2022.10060244 · Cited by 7
Klyuchnikov, N., Ismailova, L., Kovalev, D., Safonov, S., & Koroteev, D. A. (2022) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2022.110106 · Cited by 7
A Methodology for Extracting Power-Efficient and Contrast Enhanced RGB Images
Dritsas, E., & Τρίγκα, Μ. (2022) · Sensors · doi:10.3390/s22041461 · Cited by 7
Ghojogh, B., Karray, F., & Crowley, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-50516-5_10 · Cited by 7
Approximating Human Judgment of Generated Image QualityPreprint
Kolchinski, Y. A., Zhou, S., Zhao, S., Gordon, M., & Ermon, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12121 · Cited by 7
Enhancing Multi-Channel Eeg Classification with Gramian Temporal Generative Adversarial Networks
Kan, C. N. E., Povinelli, R. J., & Ye, D. H. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414078 · Cited by 6
Wu, Y. C., Bai, Y., Lan, Z., & Yao, S. (2023) · International Journal of Computational Methods · doi:10.1142/s0219876223410074 · Cited by 6
Early prediction for mode anomaly in generative adversarial network training: An empirical study
Guo, C., Huang, D., Zhang, J., Xu, J., Bai, G., & Dong, N. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.046 · Cited by 6
Task-Aware Novelty Detection for Visual-based Deep Learning in Autonomous Systems
Chen, V., Yoon, M.-K., & Shao, Z. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9196720 · Cited by 6
Gill, H. S., Singh, M. L., Singh, M., Priyanka, Kaur, S., & Kaur, H. (2023) · International Journal of Communication Systems · doi:10.1002/dac.5426 · Cited by 6
Dragan, C.-M., Saad, M. M., Rehmani, M. H., & O’Reilly, R. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-46341-9_4 · Cited by 6
Metadata
- Authors
- Snell, J., Ridgeway, K., Liao, R., Roads, B. D., Mozer, M. C., & Zemel, R. S.
- Affiliations
- University of Toronto (CA)
- University of Colorado Boulder (US)
- Venue
- 2017 IEEE International Conference on Image Processing (ICIP)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 4277-4281
- ISSN
- 1522-4880 (ISSN-L)
- OpenAlex
- W2174358748
- Semantic Scholar
- 90e4f12fa8fb126d0f416c8b61bfb5f73f8b7b74
- Cited by
- 186
- References
- 46
- Citations by year
- 2026: 15 · 2025: 23 · 2024: 21 · 2023: 21 · 2022: 17 · 2021: 22 · 2020: 28 · 2019: 16 · 2018: 11 · 2017: 7
- FWCI
- 4.96
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - International Conference on Image Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (0542013, 1041755, 1461535)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Keywords
- perceptual image quality, MS-SSIM, image generation, loss function design, autoencoder, image reconstruction, image classification, super-resolution, human perception
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
