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 2826–2850 of 10307 papers

TitleStatusHype
Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning—0
Distributionally Robust Transfer Learning—0
A 3M-Hybrid Model for the Restoration of Unique Giant Murals: A Case Study on the Murals of Yongle Palace—0
Transferability analysis of data-driven additive manufacturing knowledge: a case study between powder bed fusion and directed energy deposition—0
A Strong and Simple Deep Learning Baseline for BCI MI DecodingCode1
SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition—0
A physics-informed and attention-based graph learning approach for regional electric vehicle charging demand predictionCode2
Generalized Graphon Process: Convergence of Graph Frequencies in Stretched Cut Distance—0
Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity RecognitionCode0
MultIOD: Rehearsal-free Multihead Incremental Object Detector—0
DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuningCode1
A Novel Training Framework for Physics-informed Neural Networks: Towards Real-time Applications in Ultrafast Ultrasound Blood Flow Imaging—0
Video and Synthetic MRI Pre-training of 3D Vision Architectures for Neuroimage Analysis—0
Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection—0
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option PricingCode0
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays—0
Overcoming Data Limitations: A Few-Shot Specific Emitter Identification Method Using Self-Supervised Learning and Adversarial AugmentationCode1
Enabling Intelligent Vehicular Networks Through Distributed Learning in the Non-Terrestrial Networks 6G Vision—0
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens—0
Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillation—0
Active shooter detection and robust tracking utilizing supplemental synthetic data—0
EvoCLINICAL: Evolving Cyber-Cyber Digital Twin with Active Transfer Learning for Automated Cancer Registry SystemCode0
Adaptive Growth: Real-time CNN Layer ExpansionCode0
Roulette: A Semantic Privacy-Preserving Device-Edge Collaborative Inference Framework for Deep Learning Classification Tasks—0
Probabilistic Self-supervised Learning via Scoring Rules Minimization—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