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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 10051–10075 of 10307 papers

TitleStatusHype
Quantitative Imaging Principles Improves Medical Image LearningCode0
Quantum Computing Supported Adversarial Attack-Resilient Autonomous Vehicle Perception Module for Traffic Sign ClassificationCode0
Query-based Targeted Action-Space Adversarial Policies on Deep Reinforcement Learning AgentsCode0
Question Answering through Transfer Learning from Large Fine-grained Supervision DataCode0
Quick, get me a Dr. BERT: Automatic Grading of Evidence using Transfer LearningCode0
QWID: Quantized Weed Identification Deep neural networkCode0
RaMen: Multi-Strategy Multi-Modal Learning for Bundle ConstructionCode0
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-rayCode0
Randomized Geometric Algebra Methods for Convex Neural NetworksCode0
An Adaptive Random Path Selection Approach for Incremental LearningCode0
Cross-Cultural Similarity Features for Cross-Lingual Transfer Learning of Pragmatically Motivated TasksCode0
Rapid Automated Mapping of Clouds on Titan With Instance SegmentationCode0
RAPid-Learn: A Framework for Learning to Recover for Handling Novelties in Open-World EnvironmentsCode0
Rapid morphology characterization of two-dimensional TMDs and lateral heterostructures based on deep learningCode0
RASNet: Segmentation for Tracking Surgical Instruments in Surgical Videos Using Refined Attention Segmentation NetworkCode0
Real-Time Decentralized knowledge Transfer at the EdgeCode0
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and TechniquesCode0
Real-Time Topology Optimization in 3D via Deep Transfer LearningCode0
Recipe recognition with large multimodal food datasetCode0
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI AcceleratorsCode0
RefHCM: A Unified Model for Referring Perceptions in Human-Centric ScenariosCode0
Region Invariant Normalizing Flows for Mobility TransferCode0
Regression-Oriented Knowledge Distillation for Lightweight Ship Orientation Angle Prediction with Optical Remote Sensing ImagesCode0
Regret-Optimal Federated Transfer Learning for Kernel Regression with Applications in American Option PricingCode0
Rehearsal-Free Modular and Compositional Continual Learning for Language ModelsCode0
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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