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 526–550 of 10307 papers

TitleStatusHype
MultiTACRED: A Multilingual Version of the TAC Relation Extraction DatasetCode1
PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud VideosCode1
Avatar Knowledge Distillation: Self-ensemble Teacher Paradigm with UncertaintyCode1
Semi-supervised Domain Adaptation via Prototype-based Multi-level LearningCode1
Improving Contrastive Learning of Sentence Embeddings from AI FeedbackCode1
Shotgun crystal structure prediction using machine-learned formation energiesCode1
Polyp-SAM: Transfer SAM for Polyp SegmentationCode1
π-Tuning: Transferring Multimodal Foundation Models with Optimal Multi-task InterpolationCode1
Deep Fast Vision: Accelerated Deep Transfer Learning Vision Prototyping and BeyondCode1
SCoDA: Domain Adaptive Shape Completion for Real ScansCode1
Text2Seg: Remote Sensing Image Semantic Segmentation via Text-Guided Visual Foundation ModelsCode1
RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario UnderstandingCode1
Exploring Incompatible Knowledge Transfer in Few-shot Image GenerationCode1
Model Sparsity Can Simplify Machine UnlearningCode1
The MONET dataset: Multimodal drone thermal dataset recorded in rural scenariosCode1
Selective Knowledge Sharing for Privacy-Preserving Federated Distillation without A Good TeacherCode1
I2I: Initializing Adapters with Improvised KnowledgeCode1
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge ExcavationCode1
Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ SegmentationCode1
Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake modelsCode1
Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring ModelsCode1
HOICLIP: Efficient Knowledge Transfer for HOI Detection with Vision-Language ModelsCode1
Model-Based Reinforcement Learning with Isolated ImaginationsCode1
BlackVIP: Black-Box Visual Prompting for Robust Transfer LearningCode1
Decoupled Multimodal Distilling for Emotion RecognitionCode1
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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