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 351–400 of 10307 papers

TitleStatusHype
GEM: Boost Simple Network for Glass Surface Segmentation via Segment Anything Model and Data SynthesisCode1
Pre-training and Diagnosing Knowledge Base Completion ModelsCode1
Prompt-enhanced Federated Content Representation Learning for Cross-domain RecommendationCode1
TCE at Qur'an QA 2023 Shared Task: Low Resource Enhanced Transformer-based Ensemble Approach for Qur'anic QACode1
Facing the Elephant in the Room: Visual Prompt Tuning or Full Finetuning?Code1
Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation ModelsCode1
HiCD: Change Detection in Quality-Varied Images via Hierarchical Correlation DistillationCode1
Walert: Putting Conversational Search Knowledge into Action by Building and Evaluating a Large Language Model-Powered ChatbotCode1
Transfer-Learning-Based Autotuning Using Gaussian CopulaCode1
Time-lapse seismic inversion for CO2 saturation with SeisCO2Net: An application to Frio-II siteCode1
Detection and Classification of Diabetic Retinopathy using Deep Learning Algorithms for Segmentation to Facilitate Referral Recommendation for Test and Treatment PredictionCode1
DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic SegmentationCode1
Federated Class-Incremental Learning with New-Class Augmented Self-DistillationCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
Weed mapping in multispectral drone imagery using lightweight vision transformersCode1
Soft Contrastive Learning for Time SeriesCode1
TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation LearningCode1
APTv2: Benchmarking Animal Pose Estimation and Tracking with a Large-scale Dataset and BeyondCode1
Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change DetectionCode1
Fine-tuning Graph Neural Networks by Preserving Graph Generative PatternsCode1
Zero-1-to-3: Domain-level Zero-shot Cognitive Diagnosis via One Batch of Early-bird Students towards Three Diagnostic ObjectivesCode1
A Closer Look at the Few-Shot Adaptation of Large Vision-Language ModelsCode1
Multi-Modality is All You Need for Transferable Recommender SystemsCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp SegmentationCode1
MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain AdaptationCode1
DTL: Disentangled Transfer Learning for Visual RecognitionCode1
MToP: A MATLAB Optimization Platform for Evolutionary MultitaskingCode1
Open-Pose 3D Zero-Shot Learning: Benchmark and ChallengesCode1
NVS-Adapter: Plug-and-Play Novel View Synthesis from a Single ImageCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
READ: Recurrent Adapter with Partial Video-Language Alignment for Parameter-Efficient Transfer Learning in Low-Resource Video-Language ModelingCode1
Progressive Multi-Modality Learning for Inverse Protein FoldingCode1
Labrador: Exploring the Limits of Masked Language Modeling for Laboratory DataCode1
Parameter-Efficient Transfer Learning of Audio Spectrogram TransformersCode1
A Scalable and Generalizable Pathloss Map PredictionCode1
Does Vector Quantization Fail in Spatio-Temporal Forecasting? Exploring a Differentiable Sparse Soft-Vector Quantization ApproachCode1
Strong but simple: A Baseline for Domain Generalized Dense Perception by CLIP-based Transfer LearningCode1
Knowledge Transfer from Vision Foundation Models for Efficient Training of Small Task-specific ModelsCode1
Calibration-free online test-time adaptation for electroencephalography motor imagery decodingCode1
Side4Video: Spatial-Temporal Side Network for Memory-Efficient Image-to-Video Transfer LearningCode1
Unified Domain Adaptive Semantic SegmentationCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Developing a Named Entity Recognition Dataset for TagalogCode1
Deep Fast Vision: A Python Library for Accelerated Deep Transfer Learning Vision PrototypingCode1
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
Active Transfer Learning for Efficient Video-Specific Human Pose EstimationCode1
Transfer learning from a sparsely annotated dataset of 3D medical imagesCode1
Mini but Mighty: Finetuning ViTs with Mini AdaptersCode1
Improved Child Text-to-Speech Synthesis through Fastpitch-based Transfer LearningCode1
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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