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 4226–4250 of 10307 papers

TitleStatusHype
Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks—0
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer—0
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System—0
DARE: A large-scale handwritten date recognition system—0
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation—0
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation—0
Green Resource Allocation in Cloud-Native O-RAN Enabled Small Cell Networks—0
SAPT: A Shared Attention Framework for Parameter-Efficient Continual Learning of Large Language Models—0
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World—0
Long-Tailed Learning Requires Feature Learning—0
DaNetQA: a yes/no Question Answering Dataset for the Russian Language—0
DA-Net: A Disentangled and Adaptive Network for Multi-Source Cross-Lingual Transfer Learning—0
A Comparative Study of Open Source Computer Vision Models for Application on Small Data: The Case of CFRP Tape Laying—0
GridDehazeNet+: An Enhanced Multi-Scale Network with Intra-Task Knowledge Transfer for Single Image Dehazing—0
A Conceptual Framework for Lifelong Learning—0
DAML: Chinese Named Entity Recognition with a fusion method of data-augmentation and meta-learning—0
A model is worth tens of thousands of examples—0
Damage detection using in-domain and cross-domain transfer learning—0
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images—0
A Unified Framework for Heterogeneous Semi-supervised Learning—0
3D-RADNet: Extracting labels from DICOM metadata for training general medical domain deep 3D convolution neural networks—0
Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data—0
GDA-HIN: A Generalized Domain Adaptive Model across Heterogeneous Information Networks—0
DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation—0
Amobee at IEST 2018: Transfer Learning from Language Models—0
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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