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Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 6201–6250 of 10307 papers

TitleStatusHype
Efficient Deep Learning Architectures for Fast Identification of Bacterial Strains in Resource-Constrained DevicesCode0
HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenMLCode1
TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS—0
Variational Information Bottleneck for Effective Low-Resource Fine-TuningCode1
AUGNLG: Few-shot Natural Language Generation using Self-trained Data AugmentationCode1
A multi-objective perspective on jointly tuning hardware and hyperparameters—0
Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition—0
Supervising the Transfer of Reasoning Patterns in VQA—0
Fair Normalizing FlowsCode1
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain ResponsesCode0
Probing transfer learning with a model of synthetic correlated datasets—0
Audiovisual transfer learning for audio tagging and sound event detection—0
Rethinking Transfer Learning for Medical Image ClassificationCode1
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units—0
Distilling Image Classifiers in Object DetectorsCode1
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction—0
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data—0
Predicting the Success of Domain Adaptation in Text Similarity—0
Adaptive transfer learning—0
SpaceMeshLab: Spatial Context Memoization and Meshgrid Atrous Convolution Consensus for Semantic Segmentation—0
A Deep Value-network Based Approach for Multi-Driver Order Dispatching—0
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
Multilingual Neural Semantic Parsing for Low-Resourced LanguagesCode0
Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language InferenceCode1
Equivariant Graph Neural Networks for 3D Macromolecular StructureCode1
GAN Cocktail: mixing GANs without dataset accessCode0
DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and Rendering—0
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models—0
DAMSL: Domain Agnostic Meta Score-based LearningCode0
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images—0
BiToD: A Bilingual Multi-Domain Dataset For Task-Oriented Dialogue ModelingCode1
MexPub: Deep Transfer Learning for Metadata Extraction from German Publications—0
A Survey on Deep Domain Adaptation for LiDAR Perception—0
Aligning Pretraining for Detection via Object-Level Contrastive LearningCode1
SE(3)-equivariant prediction of molecular wavefunctions and electronic densities—0
Materials Representation and Transfer Learning for Multi-Property PredictionCode1
Causality in Neural Networks -- An Extended Abstract—0
Bilingual Alignment Pre-Training for Zero-Shot Cross-Lingual TransferCode0
Language Embeddings for Typology and Cross-lingual Transfer LearningCode0
Template-Based Named Entity Recognition Using BARTCode1
Syntax-augmented Multilingual BERT for Cross-lingual TransferCode1
When Vision Transformers Outperform ResNets without Pre-training or Strong Data AugmentationsCode0
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics EducationCode1
FedHealth 2: Weighted Federated Transfer Learning via Batch Normalization for Personalized Healthcare—0
New Domain, Major Effort? How Much Data is Necessary to Adapt a Temporal Tagger to the Voice Assistant DomainCode0
Optum at MEDIQA 2021: Abstractive Summarization of Radiology Reports using simple BART Finetuning—0
NLM at MEDIQA 2021: Transfer Learning-based Approaches for Consumer Question and Multi-Answer Summarization—0
SB_NITK at MEDIQA 2021: Leveraging Transfer Learning for Question Summarization in Medical Domain—0
SAFFRON: tranSfer leArning For Food-disease RelatiOn extractioN—0
UCSD-Adobe at MEDIQA 2021: Transfer Learning and Answer Sentence Selection for Medical Summarization—0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified