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 7801–7825 of 10307 papers

TitleStatusHype
Research on Task Discovery for Transfer Learning in Deep Neural Networks—0
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning—0
Exploiting the Logits: Joint Sign Language Recognition and Spell-Correction—0
NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTubeCode0
LSTM and GPT-2 Synthetic Speech Transfer Learning for Speaker Recognition to Overcome Data Scarcity—0
\#NotAWhore! A Computational Linguistic Perspective of Rape Culture and Victimization on Social Media—0
A Survey on Self-supervised Pre-training for Sequential Transfer Learning in Neural Networks—0
Learning unbiased zero-shot semantic segmentation networks via transductive transferCode0
Predicting the Difficulty and Response Time of Multiple Choice Questions Using Transfer Learning—0
Language to Network: Conditional Parameter Adaptation with Natural Language Descriptions—0
Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks—0
Overcoming Concept Shift in Domain-Aware Settings through Consolidated Internal DistributionsCode0
POSTECH Submission on Duolingo Shared Task—0
DoQA - Accessing Domain-Specific FAQs via Conversational QA—0
Intermediate-Task Transfer Learning with Pretrained Language Models: When and Why Does It Work?—0
Efficient Neural Machine Translation for Low-Resource Languages via Exploiting Related Languages—0
In Neural Machine Translation, What Does Transfer Learning Transfer?—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Taxonomy Construction of Unseen Domains via Graph-based Cross-Domain Knowledge Transfer—0
Enabling Low-Resource Transfer Learning across COVID-19 Corpora by Combining Event-Extraction and Co-Training—0
Enhancing Cross-target Stance Detection with Transferable Semantic-Emotion Knowledge—0
Automated Scoring of Clinical Expressive Language Evaluation Tasks—0
The University of Helsinki Submission to the IWSLT2020 Offline SpeechTranslation Task—0
Unsupervised Paraphasia Classification in Aphasic Speech—0
TRANSFER :- DEEP INDUCTIVE NETWORK FOR FACIAL EMOTION RECOGNITIONCode0
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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