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 1351–1375 of 10307 papers

TitleStatusHype
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning ProcessesCode1
Leverage Your Local and Global Representations: A New Self-Supervised Learning StrategyCode1
CLiMB: A Continual Learning Benchmark for Vision-and-Language TasksCode1
AVocaDo: Strategy for Adapting Vocabulary to Downstream DomainCode1
BIOSCAN-5M: A Multimodal Dataset for Insect BiodiversityCode1
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous DatasetsCode1
Leveraging Slot Descriptions for Zero-Shot Cross-Domain Dialogue StateTrackingCode1
CLIP-Lite: Information Efficient Visual Representation Learning with Language SupervisionCode1
BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and MappingCode1
Classification of animal sounds in a hyperdiverse rainforest using Convolutional Neural NetworksCode1
Classification of Epithelial Ovarian Carcinoma Whole-Slide Pathology Images Using Deep Transfer LearningCode1
Association Graph Learning for Multi-Task Classification with Category ShiftsCode1
Linear Connectivity Reveals Generalization StrategiesCode1
ChrEn: Cherokee-English Machine Translation for Endangered Language RevitalizationCode1
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel SynthesisCode1
Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?Code1
LoGAH: Predicting 774-Million-Parameter Transformers using Graph HyperNetworks with 1/100 ParametersCode1
Long-Context Inference with Retrieval-Augmented Speculative DecodingCode1
Long-tail Augmented Graph Contrastive Learning for RecommendationCode1
BadMerging: Backdoor Attacks Against Model MergingCode1
Low-Resource Music Genre Classification with Cross-Modal Neural Model ReprogrammingCode1
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer LearningCode1
CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learningCode1
ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning ParadigmsCode1
Show:102550
← PrevPage 55 of 413Next →
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