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 876–900 of 10307 papers

TitleStatusHype
Distilling Image Classifiers in Object DetectorsCode1
Disentangled Knowledge Transfer for OOD Intent Discovery with Unified Contrastive LearningCode1
Lifelong Event Detection with Knowledge TransferCode1
Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent AlignmentCode1
Disentangled Pre-training for Human-Object Interaction DetectionCode1
Assemble Foundation Models for Automatic Code SummarizationCode1
Lipschitz Lifelong Reinforcement LearningCode1
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and ResolutionCode1
Unified Domain Adaptive Semantic SegmentationCode1
LLM-Neo: Parameter Efficient Knowledge Distillation for Large Language ModelsCode1
Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?Code1
Adversarial Self-Supervised Contrastive LearningCode1
Data-Free Model ExtractionCode1
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge ExcavationCode1
LoRAShear: Efficient Large Language Model Structured Pruning and Knowledge RecoveryCode1
Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer LearningCode1
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering TasksCode1
DDAM-PS: Diligent Domain Adaptive Mixer for Person SearchCode1
A Survey of Label-Efficient Deep Learning for 3D Point CloudsCode1
Neuro2Semantic: A Transfer Learning Framework for Semantic Reconstruction of Continuous Language from Human Intracranial EEGCode1
DeepDarts: Modeling Keypoints as Objects for Automatic Scorekeeping in Darts using a Single CameraCode1
Decoupling Representation and Classifier for Long-Tailed RecognitionCode1
MA-LoT: Multi-Agent Lean-based Long Chain-of-Thought Reasoning enhances Formal Theorem ProvingCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Diffusion Models Beat GANs on Image ClassificationCode1
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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