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 4701–4750 of 10307 papers

TitleStatusHype
A Dataset of Offensive Language in Kosovo Social Media—0
Bazinga! A Dataset for Multi-Party Dialogues Structuring—0
A Corpus for Commonsense Inference in Story Cloze Test—0
A Deep Transfer Learning Method for Cross-Lingual Natural Language Inference—0
Negation Detection in Dutch Spoken Human-Computer Conversations—0
Cross-lingual and Cross-domain Transfer Learning for Automatic Term Extraction from Low Resource Data—0
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation—0
Embeddings models for Buddhist Sanskrit—0
Domain Mismatch Doesn’t Always Prevent Cross-lingual Transfer Learning—0
Evaluation of Transfer Learning and Domain Adaptation for Analyzing German-Speaking Job Advertisements—0
ArMATH: a Dataset for Solving Arabic Math Word ProblemsCode1
Transfer Learning Methods for Domain Adaptation in Technical Logbook Datasets—0
Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text SummarizationCode0
Optimization with Access to Auxiliary InformationCode0
Evaluating Gaussian Grasp Maps for Generative Grasping Models—0
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection—0
Supervised Denoising of Diffusion-Weighted Magnetic Resonance Images Using a Convolutional Neural Network and Transfer Learning—0
Transfer without ForgettingCode1
The elements of flexibility for task-performing systems—0
Unifying Voxel-based Representation with Transformer for 3D Object DetectionCode2
Extensive Study of Multiple Deep Neural Networks for Complex Random Telegraph Signals—0
A Cross-City Federated Transfer Learning Framework: A Case Study on Urban Region Profiling—0
Multi-task Optimization Based Co-training for Electricity Consumption Prediction—0
VFed-SSD: Towards Practical Vertical Federated Advertising—0
Variational Transfer Learning using Cross-Domain Latent Modulation—0
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA—0
Underwater Acoustic Communication Channel Modeling using Reservoir Computing—0
Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware DetectionCode0
HiViT: Hierarchical Vision Transformer Meets Masked Image ModelingCode1
A General Multiple Data Augmentation Based Framework for Training Deep Neural Networks—0
Long-Tailed Learning Requires Feature Learning—0
SupMAE: Supervised Masked Autoencoders Are Efficient Vision LearnersCode1
Parameter-Efficient and Student-Friendly Knowledge Distillation—0
Looks Like Magic: Transfer Learning in GANs to Generate New Card Illustrations—0
Transfer Learning-based Channel Estimation in Orthogonal Frequency Division Multiplexing Systems Using Data-nulling Superimposed PilotsCode0
Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations—0
Multi-Source Transfer Learning for Deep Model-Based Reinforcement Learning—0
Classification of COVID-19 Patients with their Severity Level from Chest CT Scans using Transfer Learning—0
Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge TransferCode1
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning—0
Semantic-aware Dense Representation Learning for Remote Sensing Image Change DetectionCode1
Efficient textual explanations for complex road and traffic scenarios based on semantic segmentation—0
Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets—0
Transfer learning driven design optimization for inertial confinement fusion—0
Balancing Data through Data Augmentation Improves the Generality of Transfer Learning for Diabetic Retinopathy Classification—0
Pre-trained Perceptual Features Improve Differentially Private Image GenerationCode0
Overcoming Catastrophic Forgetting in Zero-Shot Cross-Lingual GenerationCode2
An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning SystemsCode0
Know Where You're Going: Meta-Learning for Parameter-Efficient Fine-Tuning—0
FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source—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