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 8601–8625 of 10307 papers

TitleStatusHype
Transferable Neural Processes for Hyperparameter Optimization—0
Recognition Of Surface Defects On Steel Sheet Using Transfer Learning—0
Video Surveillance of Highway Traffic Events by Deep Learning Architectures—0
Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning—0
Investigating Multilingual NMT Representations at Scale—0
Effective Domain Knowledge Transfer with Soft Fine-tuning—0
A Transfer Learning Approach for Network Intrusion Detection—0
Learning Action-Transferable Policy with Action EmbeddingCode0
Graph Transfer Learning via Adversarial Domain Adaptation with Graph ConvolutionCode0
ACES -- Automatic Configuration of Energy Harvesting Sensors with Reinforcement Learning—0
Tensor Analysis with n-Mode Generalized Difference SubspaceCode0
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering—0
Personalizing Smartwatch Based Activity Recognition Using Transfer Learning—0
Multimodal Deep Learning for Mental Disorders Prediction from Audio Speech Samples—0
Generalization in Transfer Learning—0
A CNN-based approach to classify cricket bowlers based on their bowling actions—0
Federated Imitation Learning: A Privacy Considered Imitation Learning Framework for Cloud Robotic Systems with Heterogeneous Sensor Data—0
Transfer Fine-Tuning: A BERT Case StudyCode0
Transfer Learning Between Related Tasks Using Expected Label ProportionsCode0
Zero-shot transfer for implicit discourse relation classification—0
Repurposing Decoder-Transformer Language Models for Abstractive Summarization—0
Leveraging Non-Conversational Tasks for Low Resource Slot Filling: Does it help?—0
NITK-IT_NLP@NSURL2019: Transfer Learning based POS Tagger for Under Resourced Bhojpuri and Magahi Language—0
Evaluating the Cross-Lingual Effectiveness of Massively Multilingual Neural Machine Translation—0
Cross-Domain Training for Goal-Oriented Conversational Agents—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