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Transfer Learning

311 papers with code · Methodology

Transfer learning is a methodology where weights from a model trained on one task are taken and either used (a) to construct a fixed feature extractor, (b) as weight initialization and/or fine-tuning.

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Latest papers with code

Meta-Learning Acquisition Functions for Transfer Learning in Bayesian Optimization

ICLR 2020 metabo-iclr2020/MetaBO

Transferring knowledge across tasks to improve data-efficiency is one of the open key challenges in the area of global optimization algorithms.

GAUSSIAN PROCESSES META-LEARNING TRANSFER LEARNING

0
01 Jan 2020

Cross-lingual Alignment vs Joint Training: A Comparative Study and A Simple Unified Framework

10 Oct 2019thespectrewithin/joint-align

We further show that our proposed framework can generalize to contextualized representations and achieves state-of-the-art results on the CoNLL cross-lingual NER benchmark.

CROSS-LINGUAL TRANSFER TRANSFER LEARNING

10
10 Oct 2019

HuggingFace's Transformers: State-of-the-art Natural Language Processing

9 Oct 2019huggingface/transformers

In this paper, we present Huggingface's Transformers library, a library for state-of-the-art NLP, making these developments available to the community by gathering state-of-the-art general-purpose pretrained models under a unified API together with an ecosystem of libraries, examples, tutorials and scripts targeting many downstream NLP tasks.

TEXT GENERATION TRANSFER LEARNING

15,235
09 Oct 2019

Fine-grained Sentiment Classification using BERT

4 Oct 2019munikarmanish/bert-sentiment

In this paper, we use a promising deep learning model called BERT to solve the fine-grained sentiment classification task.

SENTIMENT ANALYSIS TRANSFER LEARNING

4
04 Oct 2019

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

3 Oct 2019LeoYu/neural-tangent-kernel-UCI

On VOC07 testbed for few-shot image classification tasks on ImageNet with transfer learning (Goyal et al., 2019), replacing the linear SVM currently used with a Convolutional NTK SVM consistently improves performance.

FEW-SHOT IMAGE CLASSIFICATION TRANSFER LEARNING

27
03 Oct 2019

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

2 Oct 2019huggingface/transformers

As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remain challenging.

LANGUAGE MODELLING LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TRANSFER LEARNING

15,235
02 Oct 2019

Robust Few-Shot Learning with Adversarially Queried Meta-Learners

2 Oct 2019goldblum/AdversarialQuerying

Previous work on adversarially robust neural networks requires large training sets and computationally expensive training procedures.

FEW-SHOT LEARNING META-LEARNING TRANSFER LEARNING

1
02 Oct 2019

Deep learning for Chemometric and non-translational data

1 Oct 2019DTUComputeStatisticsAndDataAnalysis/Weight-Share

We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing.

TRANSFER LEARNING

1
01 Oct 2019

Breast Cancer Diagnosis with Transfer Learning and Global Pooling

26 Sep 2019sara-kassani/ICIAR_Transfer_Learning_Global_Average_Pooling

Breast cancer is one of the most common causes of cancer-related death in women worldwide.

DATA AUGMENTATION TRANSFER LEARNING

0
26 Sep 2019

Pretraining boosts out-of-domain robustness for pose estimation

24 Sep 2019AlexEMG/DeepLabCut

Deep neural networks are highly effective tools for human and animal pose estimation.

ANIMAL POSE ESTIMATION TRANSFER LEARNING

1,117
24 Sep 2019