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 3601–3650 of 10307 papers

TitleStatusHype
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
BrainTalker: Low-Resource Brain-to-Speech Synthesis with Transfer Learning using Wav2Vec 2.0—0
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated LearningCode0
Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate DatasetsCode0
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification—0
Bayesian Transfer Learning—0
Deep transfer learning for visual analysis and attribution of paintings by RaphaelCode0
H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer—0
Value Explicit Pretraining for Learning Transferable Representations—0
Empowering Dual-Level Graph Self-Supervised Pretraining with Motif DiscoveryCode0
Point Cloud Segmentation Using Transfer Learning with RandLA-Net: A Case Study on Urban Areas—0
Social Learning: Towards Collaborative Learning with Large Language Models—0
Domain adaption and physical constrains transfer learning for shale gas production—0
Federated Multi-View Synthesizing for Metaverse—0
LaViP:Language-Grounded Visual Prompts—0
AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System—0
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation—0
p-Laplacian Adaptation for Generative Pre-trained Vision-Language ModelsCode0
Semantic Segmentation Using Transfer Learning on Fisheye Images—0
Cross-Domain Robustness of Transformer-based Keyphrase Generation—0
Optimizing Dense Feed-Forward Neural Networks—0
Investigating Shallow and Deep Learning Techniques for Emotion Classification in Short Persian TextsCode0
Towards Context-Aware Domain Generalization: Understanding the Benefits and Limits of Marginal Transfer Learning—0
One Self-Configurable Model to Solve Many Abstract Visual Reasoning ProblemsCode0
Exploring Multi-Level Threats in Telegram Data with AI-Human Annotation: A Preliminary Study—0
CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer—0
Weight subcloning: direct initialization of transformers using larger pretrained ones—0
Optimizing Mario Adventures in a Constrained EnvironmentCode0
VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding—0
MmAP : Multi-modal Alignment Prompt for Cross-domain Multi-task Learning—0
Context-PEFT: Efficient Multi-Modal, Multi-Task Fine-Tuning—0
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability—0
Robust Few-Shot Named Entity Recognition with Boundary Discrimination and Correlation PurificationCode0
X4D-SceneFormer: Enhanced Scene Understanding on 4D Point Cloud Videos through Cross-modal Knowledge TransferCode0
Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things—0
Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-LearningCode0
Enhanced Q-Learning Approach to Finite-Time Reachability with Maximum Probability for Probabilistic Boolean Control Networks—0
Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions—0
Dynamic Corrective Self-Distillation for Better Fine-Tuning of Pretrained Models—0
Reacting like Humans: Incorporating Intrinsic Human Behaviors into NAO through Sound-Based Reactions to Fearful and Shocking Events for Enhanced Sociability—0
Transferring CLIP's Knowledge into Zero-Shot Point Cloud Semantic Segmentation—0
Automated Behavioral Analysis Using Instance SegmentationCode0
Understanding and Leveraging the Learning Phases of Neural Networks—0
Initialization Matters for Adversarial Transfer LearningCode0
Jumpstarting Surgical Computer Vision—0
Facial Beauty Analysis Using Distribution Prediction and CNN EnsemblesCode0
Hacking Task Confounder in Meta-LearningCode0
Mutual Enhancement of Large and Small Language Models with Cross-Silo Knowledge Transfer—0
COVID-19 Detection Using Slices Processing Techniques and a Modified Xception Classifier from Computed Tomography Images—0
PGDS: Pose-Guidance Deep Supervision for Mitigating Clothes-Changing in Person Re-IdentificationCode0
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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