Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/ExpNet

ExpNet

Reported on 16 benchmarks across 7 tasks · 2 papers · 16 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision10 results

inverse tone mappingonMSU HDR Video Reconstruction Benchmark
HDR-PSNR· 2018-03-06
34.0555
best: 35.9721 (HDRTVNet)
SOTA
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content arXiv:1803.02266
inverse tone mappingonMSU HDR Video Reconstruction Benchmark
HDR-SSIM· 2018-03-06
0.9892
best: 0.9927 (HDRTVDN)
SOTA
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content arXiv:1803.02266
inverse tone mappingonMSU HDR Video Reconstruction Benchmark
HDR-VQM· 2018-03-06
0.1942
best: 0.263 (SingleHDR)
SOTA
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content arXiv:1803.02266
Inverse-Tone-MappingonMSU HDR Video Reconstruction Benchmark
HDR-PSNR· 2018-03-06
34.0555
best: 35.9721 (HDRTVNet)
SOTA
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content arXiv:1803.02266
Inverse-Tone-MappingonMSU HDR Video Reconstruction Benchmark
HDR-SSIM· 2018-03-06
0.9892
best: 0.9927 (HDRTVDN)
SOTA
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content arXiv:1803.02266
Inverse-Tone-MappingonMSU HDR Video Reconstruction Benchmark
HDR-VQM· 2018-03-06
0.1942
best: 0.263 (SingleHDR)
SOTA
ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content arXiv:1803.02266
Face ReconstructiononREALY
all· 2018-02-02
2.306
best: 2.953 (N-3DMM)
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542
Face ReconstructiononREALY (side-view)
all· 2018-02-02
2.476
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542
3D Face ReconstructiononREALY
all· 2018-02-02
2.306
best: 2.953 (N-3DMM)
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542
3D Face ReconstructiononREALY (side-view)
all· 2018-02-02
2.476
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542

Music2 results

Facial Recognition and ModellingonREALY
all· 2018-02-02
2.306
best: 2.953 (N-3DMM)
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542
Facial Recognition and ModellingonREALY (side-view)
all· 2018-02-02
2.476
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542

Methodology2 results

3DonREALY
all· 2018-02-02
2.306
best: 2.953 (N-3DMM)
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542
3DonREALY (side-view)
all· 2018-02-02
2.476
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542

Medical2 results

3D Face ModellingonREALY
all· 2018-02-02
2.306
best: 2.953 (N-3DMM)
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542
3D Face ModellingonREALY (side-view)
all· 2018-02-02
2.476
SOTA
ExpNet: Landmark-Free, Deep, 3D Facial Expressions arXiv:1802.00542