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 851–875 of 10307 papers

TitleStatusHype
An Uncertainty-aware Transfer Learning-based Framework for Covid-19 DiagnosisCode1
Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language ModelsCode1
Learning A Single Network for Scale-Arbitrary Super-ResolutionCode1
Learning from Guided Play: A Scheduled Hierarchical Approach for Improving Exploration in Adversarial Imitation LearningCode1
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighterCode1
Learning Invariant Representation for Continual LearningCode1
AnyMatch -- Efficient Zero-Shot Entity Matching with a Small Language ModelCode1
A Simple Multi-Modality Transfer Learning Baseline for Sign Language TranslationCode1
Learning Stable Classifiers by Transferring Unstable FeaturesCode1
AnyStar: Domain randomized universal star-convex 3D instance segmentationCode1
Learning to Discover Novel Visual Categories via Deep Transfer ClusteringCode1
Learning to Localize Actions from MomentsCode1
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
SentenceMIM: A Latent Variable Language ModelCode1
DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual GroundingCode1
Learning Visual Representations for Transfer Learning by Suppressing TextureCode1
Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin PrincipleCode1
LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data AugmentationCode1
A Simple yet Effective Framework for Few-Shot Aspect-Based Sentiment AnalysisCode1
Adversarially-Trained Deep Nets Transfer Better: Illustration on Image ClassificationCode1
Diffusion Model as Representation LearnerCode1
A Simple Language Model for Task-Oriented DialogueCode1
CtrlFormer: Learning Transferable State Representation for Visual Control via TransformerCode1
CUDA: Convolution-based Unlearnable DatasetsCode1
MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and ResolutionCode1
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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