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 7476–7500 of 10307 papers

TitleStatusHype
Augmenting transferred representations for stock classification—0
Exploring the potential of transfer learning for metamodels of heterogeneous material deformationCode0
CopyPaste: An Augmentation Method for Speech Emotion Recognition—0
FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries—0
Behavior Priors for Efficient Reinforcement Learning—0
Learning to be Safe: Deep RL with a Safety Critic—0
Multi-Class Zero-Shot Learning for Artistic Material Recognition—0
Transfer Learning for Input Estimation of Vehicle Systems—0
Smartphone-Based Test and Predictive Models for Rapid, Non-Invasive, and Point-of-Care Monitoring of Ocular and Cardiovascular Complications Related to Diabetes—0
A Survey on Curriculum Learning—0
Shared Space Transfer Learning for analyzing multi-site fMRI data—0
Multilingual Speech Translation with Efficient Finetuning of Pretrained Models—0
On Transferability of Bias Mitigation Effects in Language Model Fine-Tuning—0
When Being Unseen from mBERT is just the Beginning: Handling New Languages With Multilingual Language ModelsCode0
Unsupervised Paraphrasing with Pretrained Language Models—0
Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation—0
Anchor-based Bilingual Word Embeddings for Low-Resource Languages—0
A Study of Transfer Learning in Music Source Separation—0
Matching the Clinical Reality: Accurate OCT-Based Diagnosis From Few LabelsCode0
Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education—0
A Combinatorial Perspective on Transfer LearningCode0
Network Anomaly Detection Using Federated Learning and Transfer Learning—0
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers—0
CUNI Systems for the Unsupervised and Very Low Resource Translation Task in WMT20—0
Learning Transferrable Parameters for Long-tailed Sequential User Behavior Modeling—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