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

Showing 33013325 of 10307 papers

TitleStatusHype
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
Ensemble Learning via Knowledge Transfer for CTR PredictionCode0
Ensemble of Task-Specific Language Models for Brain EncodingCode0
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion RateCode0
Enriched BERT Embeddings for Scholarly Publication ClassificationCode0
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-TuningCode0
Towards an efficient deep learning model for musical onset detectionCode0
Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 GamesCode0
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration DataCode0
Small Area Estimation of Case Growths for Timely COVID-19 Outbreak DetectionCode0
Entity-aware Cross-lingual Claim Detection for Automated Fact-checkingCode0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
Facial Beauty Analysis Using Distribution Prediction and CNN EnsemblesCode0
Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and PostsCode0
SocialIQA: Commonsense Reasoning about Social InteractionsCode0
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 Dataset Distillation via Non-Critical Region RefinementCode0
A Survey of Available Corpora for Building Data-Driven Dialogue SystemsCode0
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction TasksCode0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Continuous Detection, Rapidly React: Unseen Rumors Detection based on Continual Prompt-TuningCode0
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 Scene Classification in Cloudy Image Scenarios: A Collaborative Transfer Method with Information Regulation Mechanism using Optical Cloud-Covered and SAR Remote Sensing ImagesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified