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 3851–3900 of 10307 papers

TitleStatusHype
Prototype-based HyperAdapter for Sample-Efficient Multi-task TuningCode0
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
UNO-DST: Leveraging Unlabelled Data in Zero-Shot Dialogue State TrackingCode0
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction—0
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
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
Lifelong Sequence Generation with Dynamic Module Expansion and Adaptation—0
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds—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
BanglaNLP at BLP-2023 Task 2: Benchmarking different Transformer Models for Sentiment Analysis of Bangla Social Media PostsCode0
Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks—0
A Hybrid Transfer Learning Assisted Decision Support System for Accurate Prediction of Alzheimer Disease—0
CleftGAN: Adapting A Style-Based Generative Adversarial Network To Create Images Depicting Cleft Lip DeformityCode0
Self-supervised visual learning for analyzing firearms trafficking activities on the Web—0
Reset It and Forget It: Relearning Last-Layer Weights Improves Continual and Transfer Learning—0
Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches—0
Learning Transferable Conceptual Prototypes for Interpretable Unsupervised Domain Adaptation—0
Exploiting Semantic Localization in Highly Dynamic Wireless Networks Using Deep Homoscedastic Domain AdaptationCode0
Give and Take: Federated Transfer Learning for Industrial IoT Network Intrusion Detection—0
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains—0
GraphControl: Adding Conditional Control to Universal Graph Pre-trained Models for Graph Domain Transfer Learning—0
DeePref: Deep Reinforcement Learning For Video Prefetching In Content Delivery Networks—0
Automatic Control of Reactive Brain Computer Interfaces—0
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets—0
Advancing Transformer's Capabilities in Commonsense ReasoningCode0
Transfer learning-based physics-informed convolutional neural network for simulating flow in porous media with time-varying controlsCode0
Distributed Transfer Learning with 4th Gen Intel Xeon Processors—0
Model Tuning or Prompt Tuning? A Study of Large Language Models for Clinical Concept and Relation Extraction—0
Geometrically Aligned Transfer Encoder for Inductive Transfer in Regression Tasks—0
Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural FeaturesCode0
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning—0
Hierarchical Side-Tuning for Vision Transformers—0
Understanding Transfer Learning and Gradient-Based Meta-Learning TechniquesCode0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Investigating Continuous Learning in Spiking Neural Networks—0
Advancing Diagnostic Precision: Leveraging Machine Learning Techniques for Accurate Detection of Covid-19, Pneumonia, and Tuberculosis in Chest X-Ray Images—0
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset—0
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach—0
Enhancing Cross-Dataset Performance of Distracted Driving Detection With Score Softmax Classifier And Dynamic Gaussian Smoothing SupervisionCode0
Deep Reinforcement Learning Based Cross-Layer Design in Terahertz Mesh Backhaul Networks—0
Neutral TTS Female Voice Corpus in Brazilian Portuguese—0
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection—0
EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for Industrial IoT—0
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object ClassificationCode0
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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