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 3126–3150 of 10307 papers

TitleStatusHype
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance LearningCode0
Predicting Lung Disease Severity via Image-Based AQI Analysis using Deep Learning Techniques—0
Bridging the Bosphorus: Advancing Turkish Large Language Models through Strategies for Low-Resource Language Adaptation and Benchmarking—0
SingIt! Singer Voice Transformation—0
Enriched BERT Embeddings for Scholarly Publication ClassificationCode0
Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning MethodCode0
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data—0
Dual Relation Mining Network for Zero-Shot Learning—0
Spatial Transfer Learning with Simple MLP—0
CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls—0
Few-Shot Fruit Segmentation via Transfer LearningCode0
Stable Diffusion Dataset Generation for Downstream Classification Tasks—0
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer—0
TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer—0
GMP-TL: Gender-augmented Multi-scale Pseudo-label Enhanced Transfer Learning for Speech Emotion Recognition—0
Deep Learning and Transfer Learning Architectures for English Premier League Player Performance ForecastingCode0
SatSwinMAE: Efficient Autoencoding for Multiscale Time-series Satellite Imagery—0
Creation of Novel Soft Robot Designs using Generative AI—0
TartuNLP at EvaLatin 2024: Emotion Polarity Detection—0
Diabetic Retinopathy Detection Using Quantum Transfer Learning—0
Individual Fairness Through Reweighting and Tuning—0
CromSS: Cross-modal pre-training with noisy labels for remote sensing image segmentation—0
KITE: A Kernel-based Improved Transferability Estimation Method—0
Self-supervised Pre-training of Text RecognizersCode0
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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