Transfer Learning

Published: 09 Oct 2015 Category: deep_learning


Discriminative Transfer Learning with Tree-based Priors

How transferable are features in deep neural networks?

Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks

Learning Transferable Features with Deep Adaptation Networks

Transferring Knowledge from a RNN to a DNN

Simultaneous Deep Transfer Across Domains and Tasks

Net2Net: Accelerating Learning via Knowledge Transfer

Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping

A theoretical framework for deep transfer learning

Transfer learning using neon

Hyperparameter Transfer Learning through Surrogate Alignment for Efficient Deep Neural Network Training

What makes ImageNet good for transfer learning?

Fine-tuning a Keras model using Theano trained Neural Network & Introduction to Transfer Learning

Multi-source Transfer Learning with Convolutional Neural Networks for Lung Pattern Analysis

Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning

Optimal Transport for Deep Joint Transfer Learning

CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise

Transfer Learning with Binary Neural Networks

Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network

Born Again Neural Networks

Taskonomy: Disentangling Task Transfer Learning

Do Better ImageNet Models Transfer Better?

SOSELETO: A Unified Approach to Transfer Learning and Training with Noisy Labels

GLoMo: Unsupervisedly Learned Relational Graphs as Transferable Representations

Taskonomy: Disentangling Task Transfer Learning

One Shot Learning

One-shot Learning with Memory-Augmented Neural Networks

Matching Networks for One Shot Learning

Learning feed-forward one-shot learners [NIPS 2016] [VALSE seminar]

Generative Adversarial Residual Pairwise Networks for One Shot Learning

Few-Shot Learning

Optimization as a Model for Few-Shot Learning

Learning to Compare: Relation Network for Few-Shot Learning

Unleashing the Potential of CNNs for Interpretable Few-Shot Learning

Low-Shot Learning from Imaginary Data

Semantic Feature Augmentation in Few-shot Learning

Transductive Propagation Network for Few-shot Learning

TADAM: Task dependent adaptive metric for improved few-shot learning