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 2801–2825 of 10307 papers

TitleStatusHype
PDRL: Multi-Agent based Reinforcement Learning for Predictive Monitoring—0
Toward efficient resource utilization at edge nodes in federated learning—0
Cross-modal and Cross-domain Knowledge Transfer for Label-free 3D Segmentation—0
A Hierarchical Neural Framework for Classification and its Explanation in Large Unstructured Legal Documents—0
CLIP-based Synergistic Knowledge Transfer for Text-based Person RetrievalCode0
Solving Neural Field Equations using Physics Informed Neural NetworksCode0
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario—0
Learning Unified Distance Metric Across Diverse Data Distributions with Parameter-Efficient Transfer Learning—0
Improved Breast Cancer Diagnosis through Transfer Learning on Hematoxylin and Eosin Stained Histology Images—0
SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient ChannelsCode1
One-stage Modality Distillation for Incomplete Multimodal Learning—0
MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems—0
Salient Object Detection in Optical Remote Sensing Images Driven by TransformerCode1
Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology ImagesCode1
NineRec: A Benchmark Dataset Suite for Evaluating Transferable RecommendationCode1
Enhancing Performance, Calibration Time and Efficiency in Brain-Machine Interfaces through Transfer Learning and Wearable EEG Technology—0
Efficiently Robustify Pre-trained Models—0
Adaptive Prompt Learning with Distilled Connective Knowledge for Implicit Discourse Relation RecognitionCode0
Disentangling Spatial and Temporal Learning for Efficient Image-to-Video Transfer LearningCode1
Semantic Parsing in Limited Resource Conditions—0
Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning ApproachCode0
TransNet: A Transfer Learning-Based Network for Human Action Recognition—0
Learning from Auxiliary Sources in Argumentative Revision Classification—0
Continual Learning with Dirichlet Generative-based Rehearsal—0
Distributionally Robust Transfer Learning—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