MzsrMeta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)
Stars: ✭ 181 (+206.78%)
Mutual labels: super-resolution, meta-learning
Learning-To-Compare-For-TextLearning To Compare For Text , Few shot learning in text classification
Stars: ✭ 38 (-35.59%)
Mutual labels: meta-learning
Image Super Resolution🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
Stars: ✭ 3,293 (+5481.36%)
Mutual labels: super-resolution
pykaleKnowledge-Aware machine LEarning (KALE): accessible machine learning from multiple sources for interdisciplinary research, part of the 🔥PyTorch ecosystem
Stars: ✭ 381 (+545.76%)
Mutual labels: meta-learning
SRCNN-PyTorchPytorch framework can easily implement srcnn algorithm with excellent performance
Stars: ✭ 48 (-18.64%)
Mutual labels: super-resolution
MetaD2AOfficial PyTorch implementation of "Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets" (ICLR 2021)
Stars: ✭ 49 (-16.95%)
Mutual labels: meta-learning
Zoom Learn Zoomcomputational zoom from raw sensor data
Stars: ✭ 224 (+279.66%)
Mutual labels: super-resolution
MSG-NetDepth Map Super-Resolution by Deep Multi-Scale Guidance, ECCV 2016
Stars: ✭ 76 (+28.81%)
Mutual labels: super-resolution
CSSRCrack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution (CSSR) was accepted to international conference on MVA2021 (oral), and selected for the Best Practical Paper Award.
Stars: ✭ 50 (-15.25%)
Mutual labels: super-resolution
tensorflow-mamlTensorFlow 2.0 implementation of MAML.
Stars: ✭ 79 (+33.9%)
Mutual labels: meta-learning
Jalali-Lab-Implementation-of-RAISRImplementation of RAISR (Rapid and Accurate Image Super Resolution) algorithm in Python 3.x by Jalali Laboratory at UCLA. The implementation presented here achieved performance results that are comparable to that presented in Google's research paper (with less than ± 0.1 dB in PSNR). Just-in-time (JIT) compilation employing JIT numba is used to …
Stars: ✭ 118 (+100%)
Mutual labels: super-resolution
TEGANGenerative Adversarial Network (GAN) for physically realistic enrichment of turbulent flow fields
Stars: ✭ 60 (+1.69%)
Mutual labels: super-resolution
tespImplementation of our paper "Meta Reinforcement Learning with Task Embedding and Shared Policy"
Stars: ✭ 28 (-52.54%)
Mutual labels: meta-learning
SRDenseNet-pytorchSRDenseNet-pytorch(ICCV_2017)
Stars: ✭ 113 (+91.53%)
Mutual labels: super-resolution
SrganPhoto-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Stars: ✭ 2,641 (+4376.27%)
Mutual labels: super-resolution
Magpie将任何窗口放大至全屏
Stars: ✭ 4,478 (+7489.83%)
Mutual labels: super-resolution
Awesome-Few-shotAwesome Few-shot learning
Stars: ✭ 50 (-15.25%)
Mutual labels: meta-learning
NSLImplementation for <Neural Similarity Learning> in NeurIPS'19.
Stars: ✭ 33 (-44.07%)
Mutual labels: meta-learning
FOCAL-ICLRCode for FOCAL Paper Published at ICLR 2021
Stars: ✭ 35 (-40.68%)
Mutual labels: meta-learning