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 4276–4300 of 10307 papers

TitleStatusHype
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model—0
Augmenting transferred representations for stock classification—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Adaptive Part Learning for Fine-Grained Generalized Category Discovery: A Plug-and-Play Enhancement—0
HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
CVAE-based Re-anchoring for Implicit Discourse Relation Classification—0
Augmenting Transfer Learning with Semantic Reasoning—0
AMLN: Adversarial-based Mutual Learning Network for Online Knowledge Distillation—0
Customizing General-Purpose Foundation Models for Medical Report Generation—0
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection—0
Customizing Contextualized Language Models forLegal Document Reviews—0
Customized Video QoE Estimation with Algorithm-Agnostic Transfer Learning—0
A Minimax Game for Instance based Selective Transfer Learning—0
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning—0
Curriculum Learning in Deep Neural Networks for Financial Forecasting—0
Augmenting Offline RL with Unlabeled Data—0
Adaptive Multi-Source Causal Inference—0
Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning—0
CURO: Curriculum Learning for Relative Overgeneralization—0
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey—0
A microservice-based framework for exploring data selection in cross-building knowledge transfer—0
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression—0
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks—0
AM Flow: Adapters for Temporal Processing in Action Recognition—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