https://github.com/openai/cleverhans
Towards Deep Learning Models Resistant to Adversarial Attacks
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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/pavancm/vbliinds
https://github.com/pbaylies/stylegan-encoder
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/peck94/cann-detector
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/photosynthesis-team/piq
Mean Deviation Similarity Index: Efficient and Reliable Full-Reference Image Quality Evaluator
https://github.com/pikinder/nn-patterns
https://github.com/pytorch/captum
https://github.com/r-khanna/Visualizing-and-Understanding-Convolutional-Networks
https://github.com/revbucket/mister_ed
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/ryanxingql/image-quality-assessment-toolbox
Learning a No-Reference Quality Metric for Single-Image Super-Resolution
https://github.com/salomonhotegni/MOREL
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/sar-gupta/convisualize_nb
https://github.com/scenarri/s2m-tea
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/shashankskagnihotri/adv-corrected-ddcat-cospgd
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/shnhrtkyk/satellite-super-resolution
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/stefkim/stylegan-batik
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/subeeshvasu/2018_subeesh_epsr_eccvw
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/subpic/koniq
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
https://github.com/tejas-gokhale/AGAT
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
https://github.com/tensorflow/cleverhans
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/tetrachrome/subpixel
https://github.com/thomashopkins32/PGDAdversarialLearning
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/tianhewu/assessor360
MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment
https://github.com/tianzheng4/Distributionally-Adversarial-Attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/timothyhtimothy/fast-vqa-and-fastervqa
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
https://github.com/titu1994/neural-image-assessment
https://github.com/tristanstraub/fastai-clojure
https://github.com/truskovskiyk/nima.pytorch
https://github.com/tu184044109/BVQA_Benchmark
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/ucsb-nlp-chang/textgrad
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/utlive/BRISQUE
https://github.com/val-iisc/flss
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/vztu/BVQA_Benchmark
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/vztu/VIDEVAL
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/vztu/VIDEVAL_release
UGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
https://github.com/woctezuma/stylegan2-projecting-images
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.