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 9676–9700 of 10307 papers

TitleStatusHype
LEAP nets for power grid perturbationsCode0
Learnable Parameter SimilarityCode0
Learned Interferometric Imaging for the SPIDER InstrumentCode0
LEARNER: A Transfer Learning Method for Low-Rank Matrix EstimationCode0
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual PersistenceCode0
Learning Action-Transferable Policy with Action EmbeddingCode0
Learning Adversarially Fair and Transferable RepresentationsCode0
Learning-based sound speed estimation and aberration correction in linear-array photoacoustic imagingCode0
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot LearningCode0
Learning Constrained Dynamics with Gauss Principle adhering Gaussian ProcessesCode0
Learning Constrained Dynamics with Gauss' Principle adhering Gaussian ProcessesCode0
Learning Curriculum Policies for Reinforcement LearningCode0
Learning De-Biased Representations for Remote-Sensing ImageryCode0
Learning Fair RepresentationsCode0
Learning Features by Watching Objects MoveCode0
Capability-Aware Shared Hypernetworks for Flexible Heterogeneous Multi-Robot CoordinationCode0
Learning for Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment ClassificationCode0
Learning from Memory: Non-Parametric Memory Augmented Self-Supervised Learning of Visual FeaturesCode0
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal PredictionCode0
Learning from Similar Linear Representations: Adaptivity, Minimaxity, and RobustnessCode0
Learning from What is Already Out There: Few-shot Sign Language Recognition with Online DictionariesCode0
Methods for Stabilizing Models across Large Samples of Projects (with case studies on Predicting Defect and Project Health)Code0
Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task LearningCode0
Learning Hyperparameters via a Data-Emphasized Variational ObjectiveCode0
Learning Independent Causal MechanismsCode0
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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