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 7251–7275 of 10307 papers

TitleStatusHype
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning—0
Calibrate and Prune: Improving Reliability of Lottery Tickets Through Prediction Calibration—0
Text-Aware Adapter for Few-Shot Keyword Spotting—0
Calibrated and Robust Foundation Models for Vision-Language and Medical Image Tasks Under Distribution Shift—0
Text-Driven Image Manipulation via Semantic-Aware Knowledge Transfer—0
Text Generation Models for Luxembourgish with Limited Data: A Balanced Multilingual Strategy—0
Textile Analysis for Recycling Automation using Transfer Learning and Zero-Shot Foundation Models—0
Text mining policy: Classifying forest and landscape restoration policy agenda with neural information retrieval—0
Text recognition on images using pre-trained CNN—0
Text-Speech Language Models with Improved Cross-Modal Transfer by Aligning Abstraction Levels—0
Text-to-Code Generation with Modality-relative Pre-training—0
Text-to-Speech for Under-Resourced Languages: Phoneme Mapping and Source Language Selection in Transfer Learning—0
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning—0
CALLIC: Content Adaptive Learning for Lossless Image Compression—0
Calliffusion: Chinese Calligraphy Generation and Style Transfer with Diffusion Modeling—0
ThangDLU at #SMM4H 2024: Encoder-decoder models for classifying text data on social disorders in children and adolescents—0
CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective Models on French Biomedical Data—0
The Actor-Advisor: Policy Gradient With Off-Policy Advice—0
The Amazing World of Neural Language Generation—0
The Application of Deep Learning for Lymph Node Segmentation: A Systematic Review—0
Camera On-boarding for Person Re-identification using Hypothesis Transfer Learning—0
The ART of Transfer Learning: An Adaptive and Robust Pipeline—0
Theater Aid System for the Visually Impaired Through Transfer Learning of Spatio-Temporal Graph Convolution Networks—0
The Bayesian Approach to Continual Learning: An Overview—0
THE Benchmark: Transferable Representation Learning for Monocular Height Estimation—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