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 8001–8025 of 10307 papers

TitleStatusHype
Adaptive Invariance for Molecule Property Prediction—0
3D Printed Brain-Controlled Robot-Arm Prosthetic via Embedded Deep Learning from sEMG Sensors—0
DoQA -- Accessing Domain-Specific FAQs via Conversational QA—0
How Can We Accelerate Progress Towards Human-like Linguistic Generalization?—0
A Novel GDP Prediction Technique based on Transfer Learning using CO2 Emission Dataset—0
Understanding and Improving Information Transfer in Multi-Task Learning—0
Towards a Multi-Dataset for Complex Emotions Learning Based on Deep Neural Networks—0
Transfer learning applied to text classification in Spanish radiological reports—0
Transfer Learning for Less-Resourced Semitic Languages Speech Recognition: the Case of Amharic—0
Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives—0
Offensive Video Detection: Dataset and Baseline Results—0
Domain Adapted Distant Supervision for Pedagogically Motivated Relation Extraction—0
Being Generous with Sub-Words towards Small NMT Children—0
Improving the Language Model for Low-Resource ASR with Online Text Corpora—0
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing—0
Do Neural Ranking Models Intensify Gender Bias?Code0
Sentiment Analysis for Hinglish Code-mixed Tweets by means of Cross-lingual Word Embeddings—0
Affect inTweets: A Transfer Learning Approach—0
Adaptation of Deep Bidirectional Transformers for Afrikaans Language—0
Supervised Hypernymy Detection in Spanish through Order Embeddings—0
Aggression Identification in Social Media: a Transfer Learning Based Approach—0
Intermediate-Task Transfer Learning with Pretrained Models for Natural Language Understanding: When and Why Does It Work?—0
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian—0
Cross-lingual Structure Transfer for Zero-resource Event Extraction—0
Irony Detection in Persian Language: A Transfer Learning Approach Using Emoji Prediction—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