All Projects → parkseobin → MLSR

parkseobin / MLSR

Licence: MIT license
Source code for ECCV2020 "Fast Adaptation to Super-Resolution Networks via Meta-Learning"

Programming Languages

python
139335 projects - #7 most used programming language
shell
77523 projects

Projects that are alternatives of or similar to MLSR

Mzsr
Meta-Transfer Learning for Zero-Shot Super-Resolution (CVPR, 2020)
Stars: ✭ 181 (+206.78%)
Mutual labels:  super-resolution, meta-learning
Learning-To-Compare-For-Text
Learning 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
pykale
Knowledge-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-PyTorch
Pytorch framework can easily implement srcnn algorithm with excellent performance
Stars: ✭ 48 (-18.64%)
Mutual labels:  super-resolution
MetaD2A
Official 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 Zoom
computational zoom from raw sensor data
Stars: ✭ 224 (+279.66%)
Mutual labels:  super-resolution
MSG-Net
Depth Map Super-Resolution by Deep Multi-Scale Guidance, ECCV 2016
Stars: ✭ 76 (+28.81%)
Mutual labels:  super-resolution
CSSR
Crack 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-maml
TensorFlow 2.0 implementation of MAML.
Stars: ✭ 79 (+33.9%)
Mutual labels:  meta-learning
Jalali-Lab-Implementation-of-RAISR
Implementation 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
TEGAN
Generative Adversarial Network (GAN) for physically realistic enrichment of turbulent flow fields
Stars: ✭ 60 (+1.69%)
Mutual labels:  super-resolution
awesome-few-shot-meta-learning
awesome few shot / meta learning papers
Stars: ✭ 44 (-25.42%)
Mutual labels:  meta-learning
tesp
Implementation of our paper "Meta Reinforcement Learning with Task Embedding and Shared Policy"
Stars: ✭ 28 (-52.54%)
Mutual labels:  meta-learning
SRDenseNet-pytorch
SRDenseNet-pytorch(ICCV_2017)
Stars: ✭ 113 (+91.53%)
Mutual labels:  super-resolution
Srgan
Photo-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-shot
Awesome Few-shot learning
Stars: ✭ 50 (-15.25%)
Mutual labels:  meta-learning
NSL
Implementation for <Neural Similarity Learning> in NeurIPS'19.
Stars: ✭ 33 (-44.07%)
Mutual labels:  meta-learning
FOCAL-ICLR
Code for FOCAL Paper Published at ICLR 2021
Stars: ✭ 35 (-40.68%)
Mutual labels:  meta-learning

Fast Adaptation to Super-Resolution Networks via Meta-Learning

Source code for ECCV2020 "Fast Adaptation to Super-Resolution Networks via Meta-Learning" paper

Requirements

Check requirements.txt to install all requirements.

conda install --file requirements.txt

or

pip install -i requirements.txt

Usage

Training with Urban100 dataset

  • Download Urban100 dataset here
  • Set Urban100 dataset directory name to Urban100 and run ./split_urban100.sh
  • Download IDN pretrained weights checkpoint_x2 here
  • Start training
python main.py --param-restore-path checkpoint_x2 --param-save-path mlsr_test_parameter
  • You can also train with other dataset by using --train-dataset flag.

Citation

If you find MLSR helpful, please consider citing our paper:

@article{park2020fast,
    title={Fast Adaptation to Super-Resolution Networks via Meta-Learning},
    author={Park, Seobin and Yoo, Jinsu and Cho, Donghyeon and Kim, Jiwon and Kim, Tae Hyun},
    journal={ECCV},
    year={2020}
}
Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].