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 6151–6175 of 10307 papers

TitleStatusHype
Multilingual Document-Level Translation Enables Zero-Shot Transfer From Sentences to Documents—0
Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses—0
A Meta-Learning Approach for Few-Shot (Dis)Agreement Identification in Online Discussions—0
Gradient Sparsification For Masked Fine-Tuning of Transformers—0
Shallow Parsing for Nepal Bhasa Complement Clauses—0
Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages—0
An Unsupervised Multiple-Task and Multiple-Teacher Model for Cross-lingual Named Entity RecognitionCode0
Hierarchical transfer learning with applications for electricity load forecastingCode0
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method—0
A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets—0
Persian Natural Language Inference: A Meta-learning approach—0
Speech Synthesis for Low Resource Languages using Transliteration Enabled Transfer Learning—0
Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking—0
Synonyms, Antonyms and Beyond—0
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
Continual Prompt Tuning for Dialog State Tracking—0
Meta-Adapter: Parameter Efficient Few-Shot Learning through Meta-Learning—0
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models—0
IDPG: An Instance-Dependent Prompt Generation Method—0
Offensive Text Detection Across Languages and Datasets Using Rule-based and Hybrid Methods—0
Leveraging Visual Knowledge in Language Tasks: An Empirical Study on Intermediate Pre-training for Cross-Modal Knowledge Transfer—0
Freezing the Pivot for Triangular Machine Translation—0
Multi-Stage Framework with Refinement based Point Set Registration for Unsupervised Bi-Lingual Word Alignment—0
IndicBART: A Pre-trained Model for Indic Natural Language Generation—0
Data-adaptive Transfer Learning for Low-resource Translation: A Case Study in Haitian—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