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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6826–6850 of 10307 papers

TitleStatusHype
Sequential learning based PINNs to overcome temporal domain complexities in unsteady flow past flapping wings—0
Sequential PatchCore: Anomaly Detection for Surface Inspection using Synthetic Impurities—0
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning—0
Sequential Transfer Learning to Decode Heard and Imagined Timbre from fMRI Data—0
Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2—0
Sexism Identification in Tweets and Gabs using Deep Neural Networks—0
Boosted Zero-Shot Learning with Semantic Correlation Regularization—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
Boosting Automatic COVID-19 Detection Performance with Self-Supervised Learning and Batch Knowledge Ensembling—0
SGIA: Enhancing Fine-Grained Visual Classification with Sequence Generative Image Augmentation—0
Shallow Parsing for Nepal Bhasa Complement Clauses—0
Shallow Parsing for Nepal Bhasa Complement Clauses—0
Boosting Convolutional Neural Networks' Protein Binding Site Prediction Capacity Using SE(3)-invariant transformers, Transfer Learning and Homology-based Augmentation—0
Shape-aware Generative Adversarial Networks for Attribute Transfer—0
Adapting Models to Signal Degradation using Distillation—0
Shared Growth of Graph Neural Networks via Prompted Free-direction Knowledge Distillation—0
Shared Learning : Enhancing Reinforcement in Q-Ensembles—0
Shared Space Transfer Learning for analyzing multi-site fMRI data—0
Boosting Deep Face Recognition via Disentangling Appearance and Geometry—0
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension—0
Sharing to learn and learning to share; Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning: A meta review—0
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers—0
Sharper Reasons: Argument Mining Leveraged with Confluent Knowledge—0
Boosting Deep Transfer Learning for COVID-19 Classification—0
Boosting Few-Shot Segmentation via Instance-Aware Data Augmentation and Local Consensus Guided Cross Attention—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