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 8601–8625 of 10307 papers

TitleStatusHype
Improving Transfer Learning with a Dual Image and Video Transformer for Multi-label Movie Trailer Genre ClassificationCode0
Using reinforcement learning to learn how to play text-based gamesCode0
Volta at SemEval-2021 Task 6: Towards Detecting Persuasive Texts and Images using Textual and Multimodal EnsembleCode0
Understanding Synonymous Referring Expressions via Contrastive FeaturesCode0
Transfer Learning for Node Regression Applied to Spreading PredictionCode0
Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property PredictionCode0
A logical-based corpus for cross-lingual evaluationCode0
Volta at SemEval-2021 Task 9: Statement Verification and Evidence Finding with Tables using TAPAS and Transfer LearningCode0
Understanding the Role of Invariance in Transfer LearningCode0
Understanding the Role of Mixup in Knowledge Distillation: An Empirical StudyCode0
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
VoltaVision: A Transfer Learning model for electronic component classificationCode0
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language ModelsCode0
Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMsCode0
Trade-off between reconstruction loss and feature alignment for domain generalizationCode0
Zero-shot User Intent Detection via Capsule Neural NetworksCode0
Under the Cover Infant Pose Estimation using Multimodal DataCode0
Mutual Information-driven Subject-invariant and Class-relevant Deep Representation Learning in BCICode0
Towards Training Music Taggers on Synthetic DataCode0
Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial IntelligenceCode0
Transfer learning for music classification and regression tasksCode0
UniBridge: A Unified Approach to Cross-Lingual Transfer Learning for Low-Resource LanguagesCode0
Towards Symbolic Reinforcement Learning with Common SenseCode0
Transfer Learning for Molecular Property Predictions from Small Data SetsCode0
UniCrossAdapter: Multimodal Adaptation of CLIP for Radiology Report GenerationCode0
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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