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 1326–1350 of 10307 papers

TitleStatusHype
AdaRank: Disagreement Based Module Rank Prediction for Low-rank AdaptationCode0
A Multi-Task and Multi-Label Classification Model for Implicit Discourse Relation Recognition—0
Unsupervised Transfer Learning via Adversarial Contrastive Training—0
Inverse design with conditional cascaded diffusion models—0
CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk—0
Tuning a SAM-Based Model with Multi-Cognitive Visual Adapter to Remote Sensing Instance Segmentation—0
Enhancement of price trend trading strategies via image-induced importance weightsCode1
Applying Deep Neural Networks to automate visual verification of manual bracket installations in aerospace—0
An Efficient and Explainable Transformer-Based Few-Shot Learning for Modeling Electricity Consumption Profiles Across Thousands of DomainsCode0
DaRec: A Disentangled Alignment Framework for Large Language Model and Recommender System—0
Training Spatial-Frequency Visual Prompts and Probabilistic Clusters for Accurate Black-Box Transfer Learning—0
Improved transferability of self-supervised learning models through batch normalization finetuningCode0
SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-trainingCode2
BadMerging: Backdoor Attacks Against Model MergingCode1
PolyCL: Contrastive Learning for Polymer Representation Learning via Explicit and Implicit AugmentationsCode1
Object Tracking Incorporating Transfer Learning into Unscented and Cubature Kalman Filters—0
Surrogate-Assisted Search with Competitive Knowledge Transfer for Expensive OptimizationCode0
Spectrum Prediction With Deep 3D Pyramid Vision Transformer LearningCode0
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out StrategiesCode1
Approaches for enhancing extrapolability in process-based and data-driven models in hydrology—0
LipidBERT: A Lipid Language Model Pre-trained on METiS de novo Lipid Library—0
Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization—0
InfLocNet: Enhanced Lung Infection Localization and Disease Detection from Chest X-Ray Images Using Lightweight Deep Learning—0
Optimizing Vision Transformers with Data-Free Knowledge Transfer—0
Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems—0
Show:102550
← PrevPage 54 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