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 1251–1275 of 10307 papers

TitleStatusHype
When Does Visual Prompting Outperform Linear Probing for Vision-Language Models? A Likelihood PerspectiveCode0
PMT-MAE: Dual-Branch Self-Supervised Learning with Distillation for Efficient Point Cloud Classification—0
Dynamic Guidance Adversarial Distillation with Enhanced Teacher KnowledgeCode0
Temporal Order Preserved Optimal Transport-based Cross-modal Knowledge Transfer Learning for ASR—0
Low-Resolution Face Recognition via Adaptable Instance-Relation Distillation—0
Adaptive Explicit Knowledge Transfer for Knowledge Distillation—0
A multilingual training strategy for low resource Text to Speech—0
Beyond Efficiency: Molecular Data Pruning for Enhanced Generalization—0
MARS: Matching Attribute-aware Representations for Text-based Sequential RecommendationCode1
Equitable Skin Disease Prediction Using Transfer Learning and Domain Adaptation—0
Multiscale Color Guided Attention Ensemble Classifier for Age-Related Macular Degeneration using Concurrent Fundus and Optical Coherence Tomography Images—0
Foundations of Multivariate Distributional Reinforcement Learning—0
Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification—0
RevCD -- Reversed Conditional Diffusion for Generalized Zero-Shot Learning—0
Aligning Medical Images with General Knowledge from Large Language ModelsCode1
An Empirical Study of Scaling Laws for Transfer—0
Disease Classification and Impact of Pretrained Deep Convolution Neural Networks on Diverse Medical Imaging Datasets across Imaging Modalities—0
Contrastive Learning with Synthetic PositivesCode1
Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative StudyCode0
CNN Based Detection of Cardiovascular Diseases from ECG Images—0
Theoretical Insights into Overparameterized Models in Multi-Task and Replay-Based Continual LearningCode0
Assessing Large Language Models for Online Extremism Research: Identification, Explanation, and New Knowledge—0
Efficient Transfer Learning Framework for Cross-Domain Click-Through Rate Prediction—0
On Transfer Learning for a Fully Convolutional Deep Neural SIMO Receiver—0
Adaptive Sample Aggregation In Transfer Learning—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