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 1226–1250 of 10307 papers

TitleStatusHype
Class-relation Knowledge Distillation for Novel Class DiscoveryCode1
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language ProcessingCode1
Byakto Speech: Real-time long speech synthesis with convolutional neural network: Transfer learning from English to BanglaCode1
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated LearningCode1
A unified framework for dataset shift diagnosticsCode1
A Strong and Simple Deep Learning Baseline for BCI MI DecodingCode1
A Unified Framework for Domain Adaptive Pose EstimationCode1
DeepGaze IIE: Calibrated prediction in and out-of-domain for state-of-the-art saliency modelingCode1
FedSSA: Semantic Similarity-based Aggregation for Efficient Model-Heterogeneous Personalized Federated LearningCode1
A Unified Framework for Microscopy Defocus Deblur with Multi-Pyramid Transformer and Contrastive LearningCode1
Few-shot Image Generation via Adaptation-Aware Kernel ModulationCode1
BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load ForecastingCode1
A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositionsCode1
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware ModellingCode1
FinEAS: Financial Embedding Analysis of SentimentCode1
Finetune like you pretrain: Improved finetuning of zero-shot vision modelsCode1
Fine-tuning giant neural networks on commodity hardware with automatic pipeline model parallelismCode1
Authorship Style Transfer with Policy OptimizationCode1
Broken Neural Scaling LawsCode1
Fine-Tuning Transformers: Vocabulary TransferCode1
Finite Element Neural Network Interpolation. Part I: Interpretable and Adaptive Discretization for Solving PDEsCode1
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
Florence: A New Foundation Model for Computer VisionCode1
FNet: Mixing Tokens with Fourier TransformsCode1
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved TransferabilityCode1
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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