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 6526–6550 of 10307 papers

TitleStatusHype
Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health—0
Risk-Aware Transfer in Reinforcement Learning using Successor Features—0
Adaptive Deep Learning for Entity Resolution by Risk Analysis—0
Risk of Transfer Learning and its Applications in Finance—0
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning—0
ADAPT : Awesome Domain Adaptation Python Toolbox—0
Best Practices for Learning Domain-Specific Cross-Lingual Embeddings—0
RNN Fisher Vectors for Action Recognition and Image Annotation—0
RoadScan: A Novel and Robust Transfer Learning Framework for Autonomous Pothole Detection in Roads—0
RoBERT -- A Romanian BERT Model—0
robo-gym -- An Open Source Toolkit for Distributed Deep Reinforcement Learning on Real and Simulated Robots—0
Best Practices in Convolutional Networks for Forward-Looking Sonar Image Recognition—0
Robotic and Generative Adversarial Attacks in Offline Writer-independent Signature Verification—0
Robotic self-representation improves manipulation skills and transfer learning—0
Robot Policy Transfer with Online Demonstrations: An Active Reinforcement Learning Approach—0
Robots Learn Increasingly Complex Tasks with Intrinsic Motivation and Automatic Curriculum Learning—0
Robust Activity Recognition for Adaptive Worker-Robot Interaction using Transfer Learning—0
Robust Adaptation of Foundation Models with Black-Box Visual Prompting—0
Robust agents learn causal world models—0
Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification—0
Better Transfer Learning with Inferred Successor Maps—0
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework—0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM—0
Robust Audio-Visual Instance Discrimination—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