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 2726–2750 of 10307 papers

TitleStatusHype
Transfer Operator Learning with Fusion Frame—0
Multichannel Attention Networks with Ensembled Transfer Learning to Recognize Bangla Handwritten Charecter—0
The Evolution of Reinforcement Learning in Quantitative Finance: A Survey—0
Advancing Voice Cloning for Nepali: Leveraging Transfer Learning in a Low-Resource Language—0
Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer DetectionCode0
Weakly Supervised Pretraining and Multi-Annotator Supervised Finetuning for Facial Wrinkle Detection—0
Electron-nucleus cross sections from transfer learning—0
Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition—0
Continual Dialogue State Tracking via Reason-of-Select DistillationCode0
CLIP-CID: Efficient CLIP Distillation via Cluster-Instance Discrimination—0
SA-GDA: Spectral Augmentation for Graph Domain Adaptation—0
Computational strategies for cross-species knowledge transfer and translational biomedicine—0
Efficient Task Transfer for HLS DSE—0
CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk—0
Inverse design with conditional cascaded diffusion models—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
Tuning a SAM-Based Model with Multi-Cognitive Visual Adapter to Remote Sensing Instance Segmentation—0
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
Improved transferability of self-supervised learning models through batch normalization finetuningCode0
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
Approaches for enhancing extrapolability in process-based and data-driven models in hydrology—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