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 3501–3525 of 10307 papers

TitleStatusHype
A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service InferenceCode0
LaCViT: A Label-aware Contrastive Fine-tuning Framework for Vision TransformersCode0
Learning from Similar Linear Representations: Adaptivity, Minimaxity, and RobustnessCode0
Cross-Cultural Transfer Learning for Chinese Offensive Language Detection—0
HybridCVLNet: A Hybrid CSI Feedback System and its Domain Adaptation—0
Gaze-based Attention Recognition for Human-Robot Collaboration—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Towards Understanding the Effect of Pretraining Label Granularity—0
Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ SegmentationCode1
Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring ModelsCode1
Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake modelsCode1
Scalable handwritten text recognition system for lexicographic sources of under-resourced languages and alphabets—0
HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language ModelsCode1
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning—0
Generalizable Local Feature Pre-training for Deformable Shape AnalysisCode0
Model-Based Reinforcement Learning with Isolated ImaginationsCode1
Galaxy Classification Using Transfer Learning and Ensemble of CNNs With Multiple Colour Spaces—0
Deep transfer learning for detecting Covid-19, Pneumonia and Tuberculosis using CXR images -- A Review—0
Guided Transfer LearningCode0
Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing—0
Δ-Patching: A Framework for Rapid Adaptation of Pre-trained Convolutional Networks without Base Performance Loss—0
BlackVIP: Black-Box Visual Prompting for Robust Transfer LearningCode1
Task-Attentive Transformer Architecture for Continual Learning of Vision-and-Language Tasks Using Knowledge Distillation—0
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection—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