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 9301–9325 of 10307 papers

TitleStatusHype
A Neural Network based Framework for Effective Laparoscopic Video Quality AssessmentCode0
End-to-End Deep Learning of Optimization HeuristicsCode0
End-to-End Video Question-Answer Generation with Generator-Pretester NetworkCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing SupervisionCode0
Enhancing Dataset Distillation via Non-Critical Region RefinementCode0
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction TasksCode0
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge TransferCode0
Enhancing Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer LearningCode0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Enhancing Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing ImagesCode0
Enhancing textual textbook question answering with large language models and retrieval augmented generationCode0
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-TuningCode0
Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 GamesCode0
Enriched BERT Embeddings for Scholarly Publication ClassificationCode0
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration DataCode0
Ensemble Learning via Knowledge Transfer for CTR PredictionCode0
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
Ensemble of Task-Specific Language Models for Brain EncodingCode0
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion RateCode0
Entity-aware Cross-lingual Claim Detection for Automated Fact-checkingCode0
Environment Invariant Linear Least SquaresCode0
EPRNet: Efficient Pyramid Representation Network for Real-Time Street Scene SegmentationCode0
Equivariant Learning of Stochastic Fields: Gaussian Processes and Steerable Conditional Neural ProcessesCode0
Establishing Deep InfoMax as an effective self-supervised learning methodology in materials informaticsCode0
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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