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 1501–1525 of 10307 papers

TitleStatusHype
Advancing Compressed Video Action Recognition through Progressive Knowledge DistillationCode0
Soft Language Prompts for Language TransferCode0
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation—0
Towards Training Music Taggers on Synthetic DataCode0
AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scansCode2
MIREncoder: Multi-modal IR-based Pretrained Embeddings for Performance Optimizations—0
PCAPVision: PCAP-Based High-Velocity and Large-Volume Network Failure Detection—0
Neuro-Symbolic Fusion of Wi-Fi Sensing Data for Passive Radar with Inter-Modal Knowledge TransferCode0
Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation—0
Bridging the Gap: Transfer Learning from English PLMs to Malaysian English—0
Cross-Lingual Transfer Learning for Speech Translation—0
Deep Image-to-Recipe TranslationCode0
Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via AdaptersCode0
Deepfake Audio Detection Using Spectrogram-based Feature and Ensemble of Deep Learning Models—0
M^2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension—0
Image Classification for Snow Detection to Improve Pedestrian Safety—0
A Deep Generative Framework for Joint Households and Individuals Population Synthesis—0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
LegalTurk Optimized BERT for Multi-Label Text Classification and NER—0
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models—0
Multi-task multi-constraint differential evolution with elite-guided knowledge transfer for coal mine integrated energy system dispatching—0
Minimax And Adaptive Transfer Learning for Nonparametric Classification under Distributed Differential Privacy Constraints—0
Malaria Cell Detection Using Deep Neural Networks—0
Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation—0
AstMatch: Adversarial Self-training Consistency Framework for Semi-Supervised Medical Image SegmentationCode0
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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