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 1926–1950 of 10307 papers

TitleStatusHype
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
ImageNot: A contrast with ImageNet preserves model rankingsCode0
Data-Driven Knowledge Transfer in Batch Q^* Learning—0
TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional RegressionCode0
Machine Learning Robustness: A Primer—0
LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localization—0
NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance FieldsCode2
Diagnosis of Skin Cancer Using VGG16 and VGG19 Based Transfer Learning Models—0
Transfer Learning with Point Transformers—0
Bailong: Bilingual Transfer Learning based on QLoRA and Zip-tie Embedding—0
A Survey on Multilingual Large Language Models: Corpora, Alignment, and Bias—0
Minimum-Norm Interpolation Under Covariate Shift—0
Rehearsal-Free Modular and Compositional Continual Learning for Language ModelsCode0
Transfer Learning with Reconstruction LossCode0
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding—0
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior—0
Facilitating Reinforcement Learning for Process Control Using Transfer Learning: Overview and Perspectives—0
Noise-Aware Training of Layout-Aware Language Models—0
Quantformer: from attention to profit with a quantitative transformer trading strategyCode2
Is Synthetic Image Useful for Transfer Learning? An Investigation into Data Generation, Volume, and Utilization—0
A Tulu Resource for Machine TranslationCode0
I2CKD : Intra- and Inter-Class Knowledge Distillation for Semantic Segmentation—0
Deep Learning Segmentation and Classification of Red Blood Cells Using a Large Multi-Scanner Dataset—0
Direct mineral content prediction from drill core images via transfer learning—0
Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series AnalysisCode1
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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