Displaying 1 to 19 from 19 results

transferlearning-tutorial - 《迁移学习简明手册》LaTex源码

  •    TeX

Jindong Wang et al. Transfer Learning Tutorial. 2018. 王晋东等. 迁移学习简明手册. 2018.

hub - A library for transfer learning by reusing parts of TensorFlow models.

  •    Python

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.

awesome-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.

Xvision - Chest Xray image analysis using Deep learning !

  •    Python

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.

drivebot - RL for driving a rover around

  •    Python

drivebot has two ROS specific components that need to be built. load map and add three bots...

snca.pytorch - Improving Generalization via Scalable Neighborhood Component Analysis

  •    Python

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.

transfer-mxnet - transfer learning written in mxnet

  •    Python

Unsupervised transfer learning for image classification written in mxnet. Note that this repo is only for unsupervised image classfication transfer learning.

Enso - Enso: An Open Source Library for Benchmarking Embeddings + Transfer Learning Methods

  •    Python

Enso is tool intended to provide a standard interface for the benchmarking of embedding and transfer learning methods for natural language processing tasks. Enso is compatible with Python 3.4+.

transfer-learning-text-tf - Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv

  •    Python

Tensorflow implementation of Semi-supervised Sequence Learning(https://arxiv.org/abs/1511.01432). Auto-encoder or language model is used as a pre-trained model to initialize LSTM text classification model.

learn-to-select-data - Code for Learning to select data for transfer learning with Bayesian Optimization

  •    Python

Sebastian Ruder, Barbara Plank (2017). Learning to select data for transfer learning with Bayesian Optimization. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark. We use the neural network library DyNet, which works well with networks that have dynamic structures. DyNet can be installed by following the instructions here.

sluice-networks - Code for Sluice networks: Learning what to share between loosely related tasks

  •    Python

Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, Anders Søgaard (2017). Sluice networks: Learning what to share between loosely related tasks. arXiv preprint arXiv:1705.08142. The code works with Python 3.5. The main requirement is DyNet (and its dependencies).

retrieval-2017-cam - Class-Weighted Convolutional Features for Image Retrieval

  •    Python

Image retrieval in realistic scenarios targets large dynamic datasets of unlabeled images. In these cases, training or fine-tuning a model every time new images are added to the database is neither efficient nor scalable. Convolutional neural networks trained for image classification over large datasets have been proven effective feature extractors for image retrieval. The most successful approaches are based on encoding the activations of convolutional layers, as they convey the image spatial information. In this paper, we go beyond this spatial information and propose a local-aware encoding of convolutional features based on semantic information predicted in the target image. To this end, we obtain the most discriminative regions of an image using Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the network and therefore, our approach, has the additional advantage of not requiring external information. In addition, we use CAMs to generate object proposals during an unsupervised re-ranking stage after a first fast search. Our experiments on two public available datasets for instance retrieval, Oxford5k and Paris6k, demonstrate the competitiveness of our approach outperforming the current state-of-the-art when using off-the-shelf models trained on ImageNet. A preprint of this paper is available on arXiv and in the BMVC 2017 proceedings.

mk-tfjs - Play MK.js with TensorFlow.js

  •    Javascript

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.