https://github.com/EPFL-VILAB/XDEnsembles
Towards Deep Learning Models Resistant to Adversarial Attacks
Papers with Code 아카이브
Video Quality Assessment
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Video Quality Assessment is a computer vision task aiming to mimic video-based human subjective perception. The goal is to produce a mos score, where higher score indicates better perceptual quality. Some well-known benchmarks for this task are KoNViD-1k, LIVE-VQC, YouTube-UGC and LSVQ. SROCC/PLCC/RMSE are usually used to evaluate the performance of different models.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/EndyWon/Deep-Feature-Perturbation
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/Hadisalman/robust-verify-benchmark
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Image-X-Institute/lpips_torch2tf
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/Jeffkang-94/pytorch-adversarial-attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/KnowledgeDiscovery/FaceSec
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/LONG-9621/IQA01
Image Quality Assessment: Unifying Structure and Texture Similarity
https://github.com/MS-Mind/MS-Code-06/tree/main/nima
https://github.com/MS-Mind/MS-Code-08/tree/main/nima
https://github.com/Meghraj-Webllisto/stylegan
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/Mind23-2/MindCode-101/tree/main/nima
https://github.com/Mind23-2/MindCode-3/tree/main/nima
https://github.com/MindSpore-scientific/code-9/tree/main/Zero-DCE
Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network
https://github.com/Mohanned-Elkholy/BigGan-model-inversion-with-image-transformations
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/NetraInamdar/Deep_Learning_Project
https://github.com/Niteeshaballa/fastai-practice
https://github.com/P2333/Max-Mahalanobis-Training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Puzer/stylegan-encoder
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/Reatris/Visualizing-and-Understanding-Convolutional-Networks
https://github.com/ReshmikaD/Artistic-Style-Transfer-CNN
https://github.com/RudreshVeerkhare/StyleGan
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/S-aiueo32/lpips-pytorch
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/SSL92/hyperIQA
https://github.com/SUPERSHOPxyz/stylegan3-gradient
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/SafiyaJan/Attacking-Neural-Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/SpikeKing/VQA-v2
https://github.com/TonyYaoMSU/ProjectedGradientDescent
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/TooTouch/WhiteBox-Part1
https://github.com/VQAssessment/FAST-VQA-and-FasterVQA
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
https://github.com/VainF/pytorch-msssim
https://github.com/VishaalMK/VectorDefense
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/YinDFY/PGD_for_targeted_attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Zoky-2020/Set-level_Guidance_Attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/abahram77/mnistChallenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/abahram77/mnist_challenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/albertmillan/adversarial-training-pytorch
Towards Deep Learning Models Resistant to Adversarial Attacks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.