SOTAVerified

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 6951–7000 of 10307 papers

TitleStatusHype
Learning Universal Policies via Text-Guided Video Generation—0
Learning Unsupervised Word Mapping by Maximizing Mean Discrepancy—0
Learning Unsupervised Word Translations Without Adversaries—0
Learning Visually Consistent Label Embeddings for Zero-Shot Learning—0
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images—0
Learn it or Leave it: Module Composition and Pruning for Continual Learning—0
Learn to Talk via Proactive Knowledge Transfer—0
LEEP: A New Measure to Evaluate Transferability of Learned Representations—0
Left Ventricle Quantification Using Direct Regression with Segmentation Regularization and Ensembles of Pretrained 2D and 3D CNNs—0
LegalTurk Optimized BERT for Multi-Label Text Classification and NER—0
LEKA:LLM-Enhanced Knowledge Augmentation—0
LeOCLR: Leveraging Original Images for Contrastive Learning of Visual Representations—0
Less is More: Undertraining Experts Improves Model Upcycling—0
LESS: Large Language Model Enhanced Semi-Supervised Learning for Speech Foundational Models—0
Lesson Learnt: Modularization of Deep Networks Allow Cross-Modality Reuse—0
Lessons from the Use of Natural Language Inference (NLI) in Requirements Engineering Tasks—0
Lessons learned from the NeurIPS 2021 MetaDL challenge: Backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification—0
Transfer Learning of Real Image Features with Soft Contrastive Loss for Fake Image Detection—0
Let's Focus: Focused Backdoor Attack against Federated Transfer Learning—0
Letter Sequence Labeling for Compound Splitting—0
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System—0
Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation—0
Leveraging Auxiliary Task Relevance for Enhanced Bearing Fault Diagnosis through Curriculum Meta-learning—0
Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognition—0
Leveraging Distillation Techniques for Document Understanding: A Case Study with FLAN-T5—0
Leveraging Foundation Models for Multi-modal Federated Learning with Incomplete Modality—0
Leveraging Industry 4.0 -- Deep Learning, Surrogate Model and Transfer Learning with Uncertainty Quantification Incorporated into Digital Twin for Nuclear System—0
Leveraging Local Domains for Image-to-Image Translation—0
Leveraging Medical Literature for Section Prediction in Electronic Health Records—0
Leveraging Medical Visual Question Answering with Supporting Facts—0
Leveraging Multi-Task Learning for Multi-Label Power System Security Assessment—0
Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos—0
Leveraging neural network interatomic potentials for a foundation model of chemistry—0
Leveraging Non-Conversational Tasks for Low Resource Slot Filling: Does it help?—0
Leveraging Parameter-Efficient Transfer Learning for Multi-Lingual Text-to-Speech Adaptation—0
Leveraging Pre-trained AudioLDM for Sound Generation: A Benchmark Study—0
On the Benefits of Public Representations for Private Transfer Learning under Distribution Shift—0
Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task—0
Leveraging Seq2seq Language Generation for Multi-level Product Issue Identification—0
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition—0
Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning—0
Leveraging Transfer Learning and User-Specific Updates for Rapid Training of BCI Decoders—0
Segmentation of Skin Lesions and their Attributes Using Multi-Scale Convolutional Neural Networks and Domain Specific Augmentations—0
Leveraging Transformers for StarCraft Macromanagement Prediction—0
Leveraging universality of jet taggers through transfer learning—0
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-modal Knowledge Transfer—0
Leveraging Weakly Annotated Data for Hate Speech Detection in Code-Mixed Hinglish: A Feasibility-Driven Transfer Learning Approach with Large Language Models—0
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network—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