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 801–825 of 10307 papers

TitleStatusHype
Implicit In-context LearningCode1
A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment AnalysisCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
Improving accuracy and speeding up Document Image Classification through parallel systemsCode1
Improving Candidate Generation for Low-resource Cross-lingual Entity LinkingCode1
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
Denoising Diffusion Autoencoders are Unified Self-supervised LearnersCode1
Annealing-Based Label-Transfer Learning for Open World Object DetectionCode1
A single-cell gene expression language modelCode1
Improving Implicit Feedback-Based Recommendation through Multi-Behavior AlignmentCode1
Continual Learning via Local Module CompositionCode1
Continual Learning with Knowledge Transfer for Sentiment ClassificationCode1
Anomaly Detection in Time Series with Triadic Motif Fields and Application in Atrial Fibrillation ECG ClassificationCode1
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
Abstractive Summarization of Spoken and Written Instructions with BERTCode1
Contour Knowledge Transfer for Salient Object DetectionCode1
Anonymization of labeled TOF-MRA images for brain vessel segmentation using generative adversarial networksCode1
Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer LearningCode1
Contrastive Cross-domain Recommendation in MatchingCode1
Improving Zero-Shot Generalization for CLIP with Synthesized PromptsCode1
Incremental Object Detection via Meta-LearningCode1
Contrastive Embeddings for Neural ArchitecturesCode1
Contrastive Representation DistillationCode1
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNetsCode1
A Simple Baseline for Bayesian Uncertainty in Deep 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