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 2651–2675 of 10307 papers

TitleStatusHype
Prototype-based HyperAdapter for Sample-Efficient Multi-task TuningCode0
Unlocking Emergent Modularity in Large Language ModelsCode1
Seeking Neural Nuggets: Knowledge Transfer in Large Language Models from a Parametric PerspectiveCode1
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs—0
Uncertainty-aware transfer across tasks using hybrid model-based successor feature reinforcement learning—0
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction—0
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State TrackingCode0
Structural transfer learning of non-Gaussian DAG—0
Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task LearningCode0
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification—0
Lifelong Sequence Generation with Dynamic Module Expansion and Adaptation—0
Empirical study of pretrained multilingual language models for zero-shot cross-lingual knowledge transfer in generation—0
Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related FeaturesCode0
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks—0
A Framework for Few-Shot Policy Transfer through Observation Mapping and Behavior CloningCode0
HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot FillingCode0
Scalarization for Multi-Task and Multi-Domain Learning at Scale—0
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds—0
BanglaNLP at BLP-2023 Task 2: Benchmarking different Transformer Models for Sentiment Analysis of Bangla Social Media PostsCode0
A Hybrid Transfer Learning Assisted Decision Support System for Accurate Prediction of Alzheimer Disease—0
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation—0
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning—0
CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip DeformityCode0
Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches—0
Self-supervised visual learning for analyzing firearms trafficking activities on the Web—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