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 1301–1350 of 10307 papers

TitleStatusHype
Modularized data-driven approximation of the Koopman operator and generator—0
Enhanced Infield Agriculture with Interpretable Machine Learning Approaches for Crop Classification—0
Interactive DualChecker for Mitigating Hallucinations in Distilling Large Language Models—0
Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation—0
RedWhale: An Adapted Korean LLM Through Efficient Continual Pretraining—0
Transfer Learning and the Early Estimation of Single-Photon Source Quality using Machine Learning MethodsCode0
Embedding Ordinality to Binary Loss Function for Improving Solar Flare ForecastingCode0
Domain-invariant Progressive Knowledge Distillation for UAV-based Object Detection—0
Transfer Operator Learning with Fusion Frame—0
Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter—0
ViLReF: An Expert Knowledge Enabled Vision-Language Retinal Foundation ModelCode1
TDS-CLIP: Temporal Difference Side Network for Image-to-Video Transfer LearningCode1
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
Continual Dialogue State Tracking via Reason-of-Select DistillationCode0
TaSL: Continual Dialog State Tracking via Task Skill Localization and ConsolidationCode1
Electron-nucleus cross sections from transfer learning—0
Advancing Voice Cloning for Nepali: Leveraging Transfer Learning in a Low-Resource Language—0
Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection—0
Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer DetectionCode0
GitHub is an effective platform for collaborative and reproducible laboratory researchCode1
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination—0
SA-GDA: Spectral Augmentation for Graph Domain Adaptation—0
Efficient Task Transfer for HLS DSE—0
Computational strategies for cross-species knowledge transfer and translational biomedicine—0
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
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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