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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 6451–6500 of 10307 papers

TitleStatusHype
Identification of Social-Media Platform of Videos through the Use of Shared Features—0
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation—0
Powering Comparative Classification with Sentiment Analysis via Domain Adaptive Knowledge TransferCode0
CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling—0
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-RaysCode0
Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
External knowledge transfer deployment inside a simple double agent Viterbi algorithm—0
Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyondCode0
Automatic Online Multi-Source Domain AdaptationCode0
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning—0
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer InterfacesCode0
A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms—0
CAM-loss: Towards Learning Spatially Discriminative Feature Representations—0
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image ClassificationCode0
Coarse-To-Fine And Cross-Lingual ASR Transfer—0
OptAGAN: Entropy-based finetuning on text VAE-GANCode0
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding—0
Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues—0
Cross-lingual Fine-tuning for Abstractive Arabic Text Summarization—0
BERT-PersNER: A New Model for Persian Named Entity Recognition—0
Transfer Learning for Czech Historical Named Entity Recognition—0
Using Transfer Learning to Automatically Mark L2 Writing Texts—0
Using convolutional neural networks for the classification of breast cancer imagesCode0
AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks—0
How Does Adversarial Fine-Tuning Benefit BERT?—0
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable PromptingCode0
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion—0
Sense representations for Portuguese: experiments with sense embeddings and deep neural language models—0
SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models—0
Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications—0
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times—0
Transfer Learning for Multi-lingual Tasks -- a Survey—0
Prototype-Guided Memory Replay for Continual Learning—0
A Framework for Supervised Heterogeneous Transfer Learning using Dynamic Distribution Adaptation and Manifold RegularizationCode0
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation—0
Canoe : A System for Collaborative Learning for Neural Nets—0
Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach—0
Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder—0
Geometry Based Machining Feature Retrieval with Inductive Transfer Learning—0
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference—0
YANMTT: Yet Another Neural Machine Translation Toolkit—0
TransFER: Learning Relation-aware Facial Expression Representations with Transformers—0
Multi-task learning from fixed-wing UAV images for 2D/3D city modeling—0
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?Code0
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training—0
Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and PostsCode0
Making Person Search Enjoy the Merits of Person Re-identification—0
How Transferable Are Self-supervised Features in Medical Image Classification Tasks?—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