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 3901–3925 of 10307 papers

TitleStatusHype
Decoupled Box Proposal and Featurization with Ultrafine-Grained Semantic Labels Improve Image Captioning and Visual Question Answering—0
Faster Uncertainty Quantification for Inverse Problems with Conditional Normalizing Flows—0
Fast Hierarchical Learning for Few-Shot Object Detection—0
Fast Low-parameter Video Activity Localization in Collaborative Learning Environments—0
A Multimodal German Dataset for Automatic Lip Reading Systems and Transfer Learning—0
Using LLMs to Establish Implicit User Sentiment of Software Desirability—0
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
Fast-staged CNN Model for Accurate pulmonary diseases and Lung cancer detection—0
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
Fast Whole-Brain MR Multi-Parametric Mapping with Scan-Specific Self-Supervised Networks—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
Community-based Multi-Agent Reinforcement Learning with Transfer and Active Exploration—0
FBK’s Multilingual Neural Machine Translation System for IWSLT 2017—0
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation—0
FDSNet: Finger dorsal image spoof detection network using light field camera—0
Feasibility and Transferability of Transfer Learning: A Mathematical Framework—0
Adaptive Transfer Learning for Plant Phenotyping—0
Feasibility of Transfer Learning: A Mathematical Framework—0
FlexPose: Pose Distribution Adaptation with Limited Guidance—0
Feature Adversarial Distillation for Point Cloud Classification—0
Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models—0
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models—0
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy—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