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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 226–250 of 10307 papers

TitleStatusHype
IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative ModelsCode1
TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action RecognitionCode1
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image ClassificationCode1
Revelio: Interpreting and leveraging semantic information in diffusion modelsCode1
MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating TrainingCode1
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language ModelsCode1
wav2sleep: A Unified Multi-Modal Approach to Sleep Stage Classification from Physiological SignalsCode1
ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility PredictionCode1
TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent CollaborationCode1
Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text RecognitionCode1
Hyper-Representations: Learning from Populations of Neural NetworksCode1
Domain Consistency Representation Learning for Lifelong Person Re-IdentificationCode1
GraphLoRA: Structure-Aware Contrastive Low-Rank Adaptation for Cross-Graph Transfer LearningCode1
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual RecognitionCode1
Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained ModelsCode1
MaPPER: Multimodal Prior-guided Parameter Efficient Tuning for Referring Expression ComprehensionCode1
Development and bilingual evaluation of Japanese medical large language model within reasonably low computational resourcesCode1
Data Efficient Child-Adult Speaker Diarization with Simulated ConversationsCode1
SimMAT: Exploring Transferability from Vision Foundation Models to Any Image ModalityCode1
Efficient Training of Large Vision Models via Advanced Automated Progressive LearningCode1
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
MARS: Matching Attribute-aware Representations for Text-based Sequential RecommendationCode1
Aligning Medical Images with General Knowledge from Large Language ModelsCode1
Contrastive Learning with Synthetic PositivesCode1
Show:102550
← PrevPage 10 of 413Next →
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