silencelamb's Stars
aikorea/awesome-rl
Reinforcement learning resources curated
google/active-learning
artix41/awesome-transfer-learning
Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)
hwalsuklee/awesome-deep-text-detection-recognition
A curated list of resources for text detection/recognition (optical character recognition ) with deep learning methods.
EFS-OpenSource/calibration-framework
The net:cal calibration framework is a Python 3 library for measuring and mitigating miscalibration of uncertainty estimates, e.g., by a neural network.
gorkemalgan/deep_learning_with_noisy_labels_literature
This repo consists of collection of papers and repos on the topic of deep learning by noisy labels / label noise.
lopusz/awesome-interpretable-machine-learning
pytorch/captum
Model interpretability and understanding for PyTorch
oneTaken/awesome_deep_learning_interpretability
深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)
ultralytics/yolov3
YOLOv3 in PyTorch > ONNX > CoreML > TFLite
extreme-assistant/CVPR2024-Paper-Code-Interpretation
cvpr2024/cvpr2023/cvpr2022/cvpr2021/cvpr2020/cvpr2019/cvpr2018/cvpr2017 论文/代码/解读/直播合集,极市团队整理
albumentations-team/albumentations
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
amusi/awesome-lane-detection
A paper list of lane detection.
facebookresearch/moco
PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722
ildoonet/pytorch-randaugment
Unofficial PyTorch Reimplementation of RandAugment.
RedditSota/state-of-the-art-result-for-machine-learning-problems
This repository provides state of the art (SoTA) results for all machine learning problems. We do our best to keep this repository up to date. If you do find a problem's SoTA result is out of date or missing, please raise this as an issue or submit Google form (with this information: research paper name, dataset, metric, source code and year). We will fix it immediately.
fengbintu/Neural-Networks-on-Silicon
This is originally a collection of papers on neural network accelerators. Now it's more like my selection of research on deep learning and computer architecture.
facebookarchive/caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework.
apache/mxnet
Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more