Methods › General › Loss Functions › VGG Loss
VGG Loss
Introduced by Christian Ledig et al. in Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
VGG Loss is a type of content loss introduced in the Perceptual Losses for Real-Time Style Transfer and Super-Resolution super-resolution and style transfer framework. It is an alternative to pixel-wise losses; VGG Loss attempts to be closer to perceptual similarity. The VGG loss is based on the ReLU activation layers of the pre-trained 19 layer VGG network. With ϕ_(i,j) we indicate the feature map obtained by the j-th convolution (after activation) before the i-th maxpooling layer within the VGG19 network, which we consider given. We then define the VGG loss as the euclidean distance between the feature representations of a reconstructed image G_(θ_G)(Iᴸᴿ) and the reference image Iᴴᴿ:
l_(VGG/i.j) = 1/(W_(i,j)H_(i,j))∑ₓ₌₁^(W_(i,j))∑_(y=1)^(H_(i,j))(ϕ_(i,j)(Iᴴᴿ)_(x, y) - ϕ_(i,j)(G_(θ_G)(Iᴸᴿ))_(x, y))²
Here W_(i,j) and H_(i,j) describe the dimensions of the respective feature maps within the VGG network.
Papers archive 2025-07-28
30 shown of 37, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Super-Resolution Generative Adversarial Networks based Video Enhancement 14 May 2025 · 0 repositories · arXiv:2505.10589
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Uncertainty Estimation for Super-Resolution using ESRGAN 19 Dec 2024 · 0 repositories · arXiv:2412.15439
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Deep Learning-Based CKM Construction with Image Super-Resolution 28 Oct 2024 · 1 repository · arXiv:2411.08887
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Power-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint 6 Apr 2024 · 0 repositories · arXiv:2404.04642
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Fully Data-Driven Model for Increasing Sampling Rate Frequency of Seismic Data using Super-Resolution Generative Adversarial Networks 31 Jan 2024 · 0 repositories · arXiv:2402.00153
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Texture and Noise Dual Adaptation for Infrared Image Super-Resolution 15 Nov 2023 · 1 repository · arXiv:2311.08816
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Guided Frequency Loss for Image Restoration 27 Sep 2023 · 0 repositories · arXiv:2309.15563
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A comparative analysis of SRGAN models 18 Jul 2023 · 0 repositories · arXiv:2307.09456
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A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models 12 Oct 2022 · 0 repositories · arXiv:2210.06362
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Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation 7 Sep 2022 · 1 repository · arXiv:2209.03355
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Contextual Attention Mechanism, SRGAN Based Inpainting System for Eliminating Interruptions from Images 6 Apr 2022 · 0 repositories · arXiv:2204.02591
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Image Superresolution using Scale-Recurrent Dense Network 28 Jan 2022 · 0 repositories · arXiv:2201.11998
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High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss 21 Jul 2021 · 0 repositories · arXiv:2107.09989
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Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks 17 May 2021 · 1 repository · arXiv:2105.07621
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More Reliable AI Solution: Breast Ultrasound Diagnosis Using Multi-AI Combination 7 Jan 2021 · 0 repositories · arXiv:2101.02639
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Super-resolution Guided Pore Detection for Fingerprint Recognition 10 Dec 2020 · 0 repositories · arXiv:2012.05959
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Fully Quantized Image Super-Resolution Networks 29 Nov 2020 · 1 repository · arXiv:2011.14265
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Micro CT Image-Assisted Cross Modality Super-Resolution of Clinical CT Images Utilizing Synthesized Training Dataset 20 Oct 2020 · 0 repositories · arXiv:2010.10207
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Attaining Real-Time Super-Resolution for Microscopic Images Using GAN 9 Oct 2020 · 1 repository · arXiv:2010.04634
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Journey Towards Tiny Perceptual Super-Resolution 8 Jul 2020 · 2 repositories · arXiv:2007.04356
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Perceptual Extreme Super Resolution Network with Receptive Field Block 26 May 2020 · 1 repository · arXiv:2005.12597Syntology ran 0 of 3 samples · 3 unverified
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Arbitrary Scale Super-Resolution for Brain MRI Images 5 Apr 2020 · 1 repository · arXiv:2004.02086
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Generator From Edges: Reconstruction of Facial Images 16 Feb 2020 · 0 repositories · arXiv:2002.06682
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EndoL2H: Deep Super-Resolution for Capsule Endoscopy 13 Feb 2020 · 3 repositories · arXiv:2002.05459
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An Application of Generative Adversarial Networks for Super Resolution Medical Imaging 19 Dec 2019 · 0 repositories · arXiv:1912.09507
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Anisotropic Super Resolution in Prostate MRI using Super Resolution Generative Adversarial Networks 19 Dec 2019 · 0 repositories · arXiv:1912.09497
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Image Super-Resolution Using a Wavelet-based Generative Adversarial Network 24 Jul 2019 · 1 repository · arXiv:1907.10213
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Boosting Resolution and Recovering Texture of micro-CT Images with Deep Learning 15 Jul 2019 · 0 repositories · arXiv:1907.07131
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SRGAN: Training Dataset Matters 24 Mar 2019 · 1 repository · arXiv:1903.09922
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Generative Collaborative Networks for Single Image Super-Resolution 27 Feb 2019 · 1 repository · arXiv:1902.10467
Tasks archive 2025-07-28
20 shown of 41 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Super-Resolution | 34 |
| Image Super-Resolution | 23 |
| Generative Adversarial Network | 14 |
| SSIM | 8 |
| Quantization | 2 |
| Video Super-Resolution | 2 |
| Brain Tumor Segmentation | 1 |
| CPU | 1 |
| Data Compression | 1 |
| Deep Learning | 1 |
| Denoising | 1 |
| Diversity | 1 |
| Domain Adaptation | 1 |
| Edge Detection | 1 |
| Face Hallucination | 1 |
| GPU | 1 |
| Image Classification | 1 |
| Image Compression | 1 |
| Image Enhancement | 1 |
| Image Generation | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections