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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3351–3375 of 10307 papers

TitleStatusHype
Enriched BERT Embeddings for Scholarly Publication ClassificationCode0
Ensemble Augmentation for Deep Neural Networks Using 1-D Time Series Vibration DataCode0
Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-TuningCode0
Supervised learning of random quantum circuits via scalable neural networksCode0
Enhancing Two-Player Performance Through Single-Player Knowledge Transfer: An Empirical Study on Atari 2600 GamesCode0
Ensemble Learning via Knowledge Transfer for CTR PredictionCode0
Efficient Computation Sharing for Multi-Task Visual Scene UnderstandingCode0
Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion RateCode0
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 Human Pose Estimation in Ancient Vase Paintings via Perceptually-grounded Style Transfer LearningCode0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction TasksCode0
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge TransferCode0
AdapterEM: Pre-trained Language Model Adaptation for Generalized Entity Matching using Adapter-tuningCode0
Enhancing Dataset Distillation via Non-Critical Region RefinementCode0
Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource LanguagesCode0
Enhancing textual textbook question answering with large language models and retrieval augmented generationCode0
End-to-End Video Question-Answer Generation with Generator-Pretester NetworkCode0
Efficient Entity Candidate Generation for Low-Resource LanguagesCode0
Leveraging Codebook Knowledge with NLI and ChatGPT for Zero-Shot Political Relation ClassificationCode0
End-to-End Deep Learning of Optimization HeuristicsCode0
Enhancing Brain Tumor Segmentation Using Channel Attention and Transfer learningCode0
Continual Dialogue State Tracking via Reason-of-Select DistillationCode0
Encodings for Prediction-based Neural Architecture SearchCode0
Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context AdaptationCode0
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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