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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1651–1675 of 10307 papers

TitleStatusHype
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
Towards Practical Single-shot Motion Synthesis—0
Understanding the Cross-Domain Capabilities of Video-Based Few-Shot Action Recognition Models—0
A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods—0
Phonetic Error Analysis of Raw Waveform Acoustic Models with Parametric and Non-Parametric CNNs—0
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting—0
Improvement of Applicability in Student Performance Prediction Based on Transfer Learning—0
Who Writes the Review, Human or AI?—0
A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers—0
Disentangling and Mitigating the Impact of Task Similarity for Continual LearningCode0
Learning 3D Robotics Perception using Inductive Priors—0
GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM DeploymentCode0
Efficient Systematic Reviews: Literature Filtering with Transformers & Transfer Learning—0
Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry—0
Source Code Foundation Models are Transferable Binary Analysis Knowledge BasesCode1
Federated and Transfer Learning for Cancer Detection Based on Image Analysis—0
On the Condition Monitoring of Bolted Joints through Acoustic Emission and Deep Transfer Learning: Generalization, Ordinal Loss and Super-Convergence—0
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter—0
Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge TransferCode2
Domain adaptation in small-scale and heterogeneous biological datasets—0
MDS-ViTNet: Improving saliency prediction for Eye-Tracking with Vision TransformerCode1
Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records—0
Recent Advances of Foundation Language Models-based Continual Learning: A Survey—0
Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks—0
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
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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