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 601–650 of 10307 papers

TitleStatusHype
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer LearningCode1
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
A CNN-Based Blind Denoising Method for Endoscopic ImagesCode1
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
Enhanced Gaussian Process Dynamical Models with Knowledge Transfer for Long-term Battery Degradation ForecastingCode1
CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learningCode1
BadMerging: Backdoor Attacks Against Model MergingCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Model LEGO: Creating Models Like Disassembling and Assembling Building BlocksCode1
CODE-AE: A Coherent De-confounding Autoencoder for Predicting Patient-Specific Drug Response From Cell Line TranscriptomicsCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language ProcessingCode1
Domain Adaptation with Auxiliary Target Domain-Oriented ClassifierCode1
Communication-Efficient and Privacy-Preserving Feature-based Federated Transfer LearningCode1
Comparative Evaluation of Pretrained Transfer Learning Models on Automatic Short Answer GradingCode1
Composable Sparse Fine-Tuning for Cross-Lingual TransferCode1
Compositional Language Continual LearningCode1
Computation-Efficient Knowledge Distillation via Uncertainty-Aware MixupCode1
ComSL: A Composite Speech-Language Model for End-to-End Speech-to-Text TranslationCode1
Confidence-Aware Multi-Teacher Knowledge DistillationCode1
Confidence-based Visual Dispersal for Few-shot Unsupervised Domain AdaptationCode1
Bag of Tricks for Image Classification with Convolutional Neural NetworksCode1
ConsistTL: Modeling Consistency in Transfer Learning for Low-Resource Neural Machine TranslationCode1
Bayesian Optimization with Automatic Prior Selection for Data-Efficient Direct Policy SearchCode1
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen DomainsCode1
Beyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-RefinementCode1
Amplifying Membership Exposure via Data PoisoningCode1
Alice: Proactive Learning with Teacher's Demonstrations for Weak-to-Strong GeneralizationCode1
Continual Learning with Knowledge Transfer for Sentiment ClassificationCode1
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning ProcessesCode1
Contour Knowledge Transfer for Salient Object DetectionCode1
Contrastive Cross-domain Recommendation in MatchingCode1
Contrastive Embeddings for Neural ArchitecturesCode1
Conv-Adapter: Exploring Parameter Efficient Transfer Learning for ConvNetsCode1
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data FormatCode1
Convolutional Bypasses Are Better Vision Transformer AdaptersCode1
Convolutional Neural Networks for Classification of Alzheimer's Disease: Overview and Reproducible EvaluationCode1
AVocaDo: Strategy for Adapting Vocabulary to Downstream DomainCode1
Algorithmic encoding of protected characteristics in image-based models for disease detectionCode1
CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language ModelsCode1
CreoleVal: Multilingual Multitask Benchmarks for CreolesCode1
Cross-Domain Few-Shot Semantic SegmentationCode1
MTTrans: Cross-Domain Object Detection with Mean-Teacher TransformerCode1
Adaptive Transfer Learning on Graph Neural NetworksCode1
Cross-Layer Distillation with Semantic CalibrationCode1
Auxiliary Signal-Guided Knowledge Encoder-Decoder for Medical Report GenerationCode1
Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V images for Cloud DetectionCode1
CUDA: Convolution-based Unlearnable DatasetsCode1
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with UncertaintyCode1
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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