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

Showing 11511175 of 10307 papers

TitleStatusHype
TransNAS-Bench-101: Improving Transferrability and Generalizability of Cross-Task Neural Architecture SearchCode1
IEPT: Instance-Level and Episode-Level Pretext Tasks for Few-Shot LearningCode1
Training Convolutional Neural Networks With Hebbian Principal Component AnalysisCode1
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-TuningCode1
Image Translation via Fine-grained Knowledge TransferCode1
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-ConsistencyCode1
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware ModellingCode1
Computation-Efficient Knowledge Distillation via Uncertainty-Aware MixupCode1
ISD: Self-Supervised Learning by Iterative Similarity DistillationCode1
Trex: Learning Execution Semantics from Micro-Traces for Binary SimilarityCode1
Sim-to-real reinforcement learning applied to end-to-end vehicle controlCode1
Parameter-Efficient Transfer Learning with Diff PruningCode1
Extended Few-Shot Learning: Exploiting Existing Resources for Novel TasksCode1
CEM500K – A large-scale heterogeneous unlabeled cellular electron microscopy image dataset for deep learningCode1
Leveraging Transfer Learning for Reliable Intelligence Identification on Vietnamese SNSs (ReINTEL)Code1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Progressive Network Grafting for Few-Shot Knowledge DistillationCode1
Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer LearningCode1
Cross-Layer Distillation with Semantic CalibrationCode1
Learning to Adapt to Evolving DomainsCode1
Co-Tuning for Transfer LearningCode1
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose trackingCode1
Multi-level Knowledge Distillation via Knowledge Alignment and CorrelationCode1
Mixed Information Flow for Cross-domain Sequential RecommendationsCode1
Data-Free Model ExtractionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified