We have collection of more than 1 Million open source products ranging from Enterprise product to small libraries in all platforms. We aggregate information from all open source repositories. Search and find the best for your needs. Check out projects section.
transferlearning-tutorial - 《迁移学习简明手册》LaTex源码
Jindong Wang et al. Transfer Learning Tutorial. 2018. 王晋东等. 迁移学习简明手册. 2018.
transfer-learning domain-adaptationbig_transfer - Official repository for the "Big Transfer (BiT): General Visual Representation Learning" paper
Update 18/06/2021: We release new high performing BiT-R50x1 models, which were distilled from BiT-M-R152x2, see this section. More details in our paper "Knowledge distillation: A good teacher is patient and consistent". Update 08/02/2021: We also release ALL BiT-M models fine-tuned on ALL 19 VTAB-1k datasets, see below.
deep-learning pytorch imagenet convolutional-neural-networks transfer-learning jax tensorflow2naacl_transfer_learning_tutorial - Repository of code for the tutorial on Transfer Learning in NLP held at NAACL 2019 in Minneapolis, MN, USA
The tutorial was given on June 2 at NAACL 2019 in Minneapolis, MN, USA by Sebastian Ruder, Matthew Peters, Swabha Swayamdipta and Thomas Wolf. Here is the webpage of NAACL tutorials for more information.
nlp naacl tutorial transfer-learningtransfer-learning-conv-ai - 🦄 State-of-the-Art Conversational AI with Transfer Learning
The present repo contains the code accompanying the blog post 🦄 How to build a State-of-the-Art Conversational AI with Transfer Learning. This code is a clean and commented code base with training and testing scripts that can be used to train a dialog agent leveraging transfer Learning from an OpenAI GPT and GPT-2 Transformer language model.
nlp deep-learning dialog pytorch neural-networks chatbots transfer-learning gpt gpt-2Haystack - Build a natural language interface for your data
Haystack is an end-to-end framework that enables you to build powerful and production-ready pipelines for different search use cases. Whether you want to perform Question Answering or semantic document search, you can use the State-of-the-Art NLP models in Haystack to provide unique search experiences and allow your users to query in natural language. Haystack is built in a modular fashion so that you can combine the best technology from other open-source projects like Huggingface's Transformers, Elasticsearch, or Milvus.
search nlp search-engine elasticsearch information-retrieval pytorch question-answering summarization transfer-learning ann language-model semantic-search squad bert dpr retriever neural-search natural-languageEasyTransfer - EasyTransfer is designed to make the development of transfer learning in NLP applications easier
EasyTransfer is designed to make the development of transfer learning in NLP applications easier. The literature has witnessed the success of applying deep Transfer Learning (TL) for many real-world NLP applications, yet it is not easy to build an easy-to-use TL toolkit to achieve such a goal. To bridge this gap, EasyTransfer is designed to facilitate users leveraging deep TL for NLP applications at ease. It was developed in Alibaba in early 2017, and has been used in the major BUs in Alibaba group and achieved very good results in 20+ business scenarios. It supports the mainstream pre-trained ModelZoo, including pre-trained language models (PLMs) and multi-modal models on the PAI platform, integrates the SOTA models for the mainstream NLP applications in AppZoo, and supports knowledge distillation for PLMs. EasyTransfer is very convenient for users to quickly start model training, evaluation, offline prediction, and online deployment. It also provides rich APIs to make the development of NLP and transfer learning easier.
transfer-learning knowledge-distillation bert nlp-applicationsspacy-transformers - 🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy
This package provides spaCy components and architectures to use transformer models via Hugging Face's transformers in spaCy. The result is convenient access to state-of-the-art transformer architectures, such as BERT, GPT-2, XLNet, etc. This release requires spaCy v3. For the previous version of this library, see the v0.6.x branch.
nlp machine-learning natural-language-processing google pytorch spacy openai transfer-learning language-model bert natural-language-understanding spacy-pipeline spacy-extension pytorch-model gpt-2 huggingface xlnetdeepdrive - Deepdrive is a simulator that allows anyone with a PC to push the state-of-the-art in self-driving
Note that we recorded the baseline dataset in sync mode which is much slower than async mode. Async mode probably is fine to record in, we just haven't got around to trying it out for v3.
competition control reinforcement-learning deep-learning simulation tensorflow deep-reinforcement-learning vision gym self-driving-car unreal-engine transfer-learning sensorimotorhub - A library for transfer learning by reusing parts of TensorFlow models.
TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models. In particular, it provides modules, which are pre-trained pieces of TensorFlow models that can be reused on new tasks. If you'd like to contribute to TensorFlow Hub, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.
tensorflow machine-learning transfer-learning embeddings image-classification mlawesome-transfer-learning - Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc
A list of awesome papers and cool resources on transfer learning, domain adaptation and domain-to-domain translation in general! As you will notice, this list is currently mostly focused on domain adaptation (DA), but don't hesitate to suggest resources in other subfields of transfer learning. I accept pull requests. Papers are ordered by theme and inside each theme by publication date (submission date for arXiv papers). If the network or algorithm is given a name in a paper, this one is written in bold before the paper's name.
transfer-learning domain-adaptation unsupervised-learning paper awesome-listAmazon-Forest-Computer-Vision - Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
Amazon Forest Computer Vision: Satellite Image tagging code using PyTorch / Keras with lots of PyTorch tricks
pytorch data-augmentation kaggle-competition kaggle deep-learning computer-vision keras neural-networks neural-network-example transfer-learningXvision - Chest Xray image analysis using Deep learning !
Chest Xray image analysis using Deep Learning and exploiting Deep Transfer Learning technique for it with Tensorflow. The maxpool-5 layer of a pretrained VGGNet-16(Deep Convolutional Neural Network) model has been used as the feature extractor here and then further trained on a 2-layer Deep neural network with SGD optimizer and Batch Normalization for classification of Normal vs Nodular Chest Xray Images.
deep-learning vggnet computer-vision medical-imaging chest-xray-images transfer-learning tensorflowfinetuner - Finetuning any DNN for better embedding on neural search tasks
Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks. It accompanies Jina to deliver the last mile of performance for domain-specific neural search applications. 🎛 Designed for finetuning: a human-in-the-loop deep learning tool for leveling up your pretrained models in domain-specific neural search applications.
tensorflow keras pytorch metric-learning transfer-learning pretrained-models triplet-loss paddlepaddle labeling-tool siamese-network fine-tuning finetuning few-shot-learning negative-sampling neural-search jinadrivebot - RL for driving a rover around
drivebot has two ROS specific components that need to be built. load map and add three bots...
reinforcement-learning transfer-learning ros q-learningtransfer-learning-algorithms - Implementation of many transfer learning algorithms in Python with Jupyter notebooks
Implementation of many transfer learning algorithms in Python with Jupyter notebooks
domain-adaptation transfer-learningsnca.pytorch - Improving Generalization via Scalable Neighborhood Component Analysis
This repo constains the pytorch implementation for the ECCV 2018 paper (paper). We use deep networks to learn feature representations optimized for nearest neighbor classifiers, which could generalize better for new object categories. This project is a re-investigation of Neighborhood Component Analysis (NCA) with recent technologies to make it scalable to deep networks and large-scale datasets. Much of code is extended from the previous unsupervised learning project. Please refer to this repo for more details.
computer-vision deep-learning visual-recognition nearest-neighbors few-shot-learning transfer-learning eccv-2018transfer-mxnet - transfer learning written in mxnet
Unsupervised transfer learning for image classification written in mxnet. Note that this repo is only for unsupervised image classfication transfer learning.
deep-learning transfer-learning mxnetgraph_distillation - Graph Distillation for Action Detection
Please note that this is not an officially supported Google product. In this work, we propose a method termed graph distillation that incorporates rich privileged information from a large-scale multi- modal dataset in the source domain, and improves the learning in the target domain where training data and modalities are scarce.
deep-learning action-detection video-understanding multimodal transfer-learningmk-tfjs - Play MK.js with TensorFlow.js
Source code for my article "Playing Mortal Kombat with TensorFlow.js. Transfer learning and data augmentation". You can find the post here and MK.js here.
tensorflow-js mobilenet ml cnn transfer-learning
We have large collection of open source products. Follow the tags from
Tag Cloud >>
Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
Add Projects.
