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 3301–3325 of 10307 papers

TitleStatusHype
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor SearchCode1
DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems—0
Pengi: An Audio Language Model for Audio TasksCode2
Exploring the Viability of Synthetic Query Generation for Relevance Prediction—0
Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens—0
Efficient ConvBN Blocks for Transfer Learning and BeyondCode1
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits—0
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactionsCode0
Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation—0
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment ClassificationCode0
SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational AbilitiesCode3
A Survey on Time-Series Pre-Trained ModelsCode2
Instruction Tuned Models are Quick LearnersCode0
Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study—0
AD-KD: Attribution-Driven Knowledge Distillation for Language Model CompressionCode1
Real-Time Flying Object Detection with YOLOv8Code1
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks—0
One-Prompt to Segment All Medical ImagesCode1
Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers—0
Transfer Learning for Causal Effect Estimation—0
Tailoring Instructions to Student's Learning Levels Boosts Knowledge DistillationCode1
The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech TranslationCode0
CB-HVTNet: A channel-boosted hybrid vision transformer network for lymphocyte assessment in histopathological images—0
Deep Reinforcement Learning to Maximize Arterial Usage during Extreme Congestion—0
Deep Ensembling for Perceptual Image Quality Assessment—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