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 4376–4400 of 10307 papers

TitleStatusHype
Explaining Emergent In-Context Learning as Kernel Regression—0
Transferring Fairness using Multi-Task Learning with Limited Demographic Information—0
CNN-based Methods for Object Recognition with High-Resolution Tactile SensorsCode0
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification—0
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning—0
Self-Distillation with Meta Learning for Knowledge Graph CompletionCode0
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning—0
Self-supervised representations in speech-based depression detection—0
Model-based adaptation for sample efficient transfer in reinforcement learning control of parameter-varying systems—0
DADIN: Domain Adversarial Deep Interest Network for Cross Domain Recommender Systems—0
Exploring the Viability of Synthetic Query Generation for Relevance Prediction—0
Viewing Knowledge Transfer in Multilingual Machine Translation Through a Representational Lens—0
Interpretable neural architecture search and transfer learning for understanding CRISPR/Cas9 off-target enzymatic reactionsCode0
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment ClassificationCode0
Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits—0
Benchmarking Deep Learning Frameworks for Automated Diagnosis of Ocular Toxoplasmosis: A Comprehensive Approach to Classification and Segmentation—0
Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study—0
Transfer Learning for Fine-grained Classification Using Semi-supervised Learning and Visual Transformers—0
Instruction Tuned Models are Quick LearnersCode0
G-Adapter: Towards Structure-Aware Parameter-Efficient Transfer Learning for Graph Transformer Networks—0
Transfer Learning for Causal Effect Estimation—0
Privacy-Preserving Ensemble Infused Enhanced Deep Neural Network Framework for Edge Cloud Convergence—0
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
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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