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 10101–10125 of 10307 papers

TitleStatusHype
Revisit Multimodal Meta-Learning through the Lens of Multi-Task LearningCode0
RIFLE: Backpropagation in Depth for Deep Transfer Learning through Re-Initializing the Fully-connected LayErCode0
RiWalk: Fast Structural Node Embedding via Role IdentificationCode0
RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learningCode0
Deep Transfer Learning of Pick Points on Fabric for Robot Bed-MakingCode0
Robust and Efficient Transfer Learning with Hidden-Parameter Markov Decision ProcessesCode0
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision ProcessesCode0
Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural NetworksCode0
Automated Thalamic Nuclei Segmentation Using Multi-Planar Cascaded Convolutional Neural NetworksCode0
Robust and Large-Payload DNN Watermarking via Fixed, Distribution-Optimized, WeightsCode0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationCode0
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization GuaranteesCode0
Robust Fine-Tuning of Vision-Language Models for Domain GeneralizationCode0
Robust Heterogeneous Federated Learning under Data CorruptionCode0
Robust Hybrid Classical-Quantum Transfer Learning Model for Text Classification Using GPT-Neo 125M with LoRA & SMOTE EnhancementCode0
Robust Knowledge Transfer in Tiered Reinforcement LearningCode0
Robust-Multi-Task Gradient BoostingCode0
Robust Navigation with Cross-Modal Fusion and Knowledge TransferCode0
Robustness and Diversity Seeking Data-Free Knowledge DistillationCode0
Robustness and Generalization Performance of Deep Learning Models on Cyber-Physical Systems: A Comparative StudyCode0
ROD: Reception-aware Online Distillation for Sparse GraphsCode0
Roof fall hazard detection with convolutional neural networks using transfer learningCode0
RuCCoD: Towards Automated ICD Coding in RussianCode0
S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-ResolutionCode0
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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