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 1651–1700 of 10307 papers

TitleStatusHype
Towards Practical Single-shot Motion Synthesis—0
The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry—0
Multi-Agent Transfer Learning via Temporal Contrastive Learning—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
A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers—0
Learning 3D Robotics Perception using Inductive Priors—0
Who Writes the Review, Human or AI?—0
Disentangling and Mitigating the Impact of Task Similarity for Continual LearningCode0
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
Source Code Foundation Models are Transferable Binary Analysis Knowledge BasesCode1
Federated and Transfer Learning for Cancer Detection Based on Image Analysis—0
Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry—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
MDS-ViTNet: Improving saliency prediction for Eye-Tracking with Vision TransformerCode1
Domain adaptation in small-scale and heterogeneous biological datasets—0
Enhancing Zero-Shot Facial Expression Recognition by LLM Knowledge TransferCode2
Deep Learning-based Epicenter Localization using Single-Station Strong Motion Records—0
Adaptive Multiscale Retinal Diagnosis: A Hybrid Trio-Model Approach for Comprehensive Fundus Multi-Disease Detection Leveraging Transfer Learning and Siamese Networks—0
Recent Advances of Foundation Language Models-based Continual Learning: A Survey—0
A Review and Implementation of Object Detection Models and Optimizations for Real-time Medical Mask Detection during the COVID-19 PandemicCode0
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum LearningCode0
Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation—0
Cost-Sensitive Multi-Fidelity Bayesian Optimization with Transfer of Learning Curve Extrapolation—0
MultiADE: A Multi-domain Benchmark for Adverse Drug Event Extraction—0
An adaptive transfer learning perspective on classification in non-stationary environments—0
FlashST: A Simple and Universal Prompt-Tuning Framework for Traffic PredictionCode2
XTrack: Multimodal Training Boosts RGB-X Video Object TrackersCode2
Cross-Context Backdoor Attacks against Graph Prompt LearningCode0
A Survey of Latent Factor Models in Recommender Systems—0
Can We Trust LLMs? Mitigate Overconfidence Bias in LLMs through Knowledge Transfer—0
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift—0
Enhancing Accuracy in Generative Models via Knowledge Transfer—0
Dual-State Personalized Knowledge Tracing with Emotional Incorporation—0
Transfer Learning for Diffusion Models—0
Daily Physical Activity Monitoring -- Adaptive Learning from Multi-source Motion Sensor Data—0
From Macro to Micro: Boosting micro-expression recognition via pre-training on macro-expression videos—0
Image-Text-Image Knowledge Transferring for Lifelong Person Re-Identification with Hybrid Clothing States—0
Transfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification—0
Acceleration of Grokking in Learning Arithmetic Operations via Kolmogorov-Arnold Representation—0
Mixture of Latent Experts Using Tensor Products—0
Noisy Data Meets Privacy: Training Local Models with Post-Processed Remote Queries—0
LoGAH: Predicting 774-Million-Parameter Transformers using Graph HyperNetworks with 1/100 ParametersCode1
Generation of synthetic data using breast cancer dataset and classification with resnet18—0
A transfer learning framework for weak-to-strong generalization—0
Uncovering cognitive taskonomy through transfer learning in masked autoencoder-based fMRI reconstruction—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