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 1751–1800 of 10307 papers

TitleStatusHype
Feature-based Federated Transfer Learning: Communication Efficiency, Robustness and PrivacyCode1
Transfer Learning in Pre-Trained Large Language Models for Malware Detection Based on System Calls—0
SPD-CFL: Stepwise Parameter Dropout for Efficient Continual Federated Learning—0
CloudS2Mask: A novel deep learning approach for improved cloud and cloud shadow masking in Sentinel-2 imageryCode1
Using autoencoders and deep transfer learning to determine the stellar parameters of 286 CARMENES M dwarfs—0
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning—0
Neural Collapse Meets Differential Privacy: Curious Behaviors of NoisyGD with Near-perfect Representation Learning—0
Modeling of Time-varying Wireless Communication Channel with Fading and ShadowingCode0
Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor—0
Enhancing Clinically Significant Prostate Cancer Prediction in T2-weighted Images through Transfer Learning from Breast Cancer—0
Brain MRI detection by Sematic Segmentation models- Transfer Learning approach—0
Navigating the Future of Federated Recommendation Systems with Foundation Models—0
Bridging Neuroscience and AI: Environmental Enrichment as a Model for Forward Knowledge Transfer—0
Fractals as Pre-training Datasets for Anomaly Detection and Localization—0
DARA: Domain- and Relation-aware Adapters Make Parameter-efficient Tuning for Visual GroundingCode1
Novel Class Discovery for Ultra-Fine-Grained Visual CategorizationCode1
MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CTCode2
Clustering-based Multitasking Deep Neural Network for Solar Photovoltaics Power Generation Prediction—0
Scalable Learning of Segment-Level Traffic Congestion Functions—0
Model Inversion Robustness: Can Transfer Learning Help?—0
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework—0
Encoder-Decoder Framework for Interactive Free Verses with Generation with Controllable High-Quality Rhyming—0
Exploring Vision Transformers for 3D Human Motion-Language Models with Motion Patches—0
Deep learning-based variational autoencoder for classification of quantum and classical states of light—0
Deep Learning Method to Predict Wound Healing Progress Based on Collagen Fibers in Wound Tissue—0
Large Language Model Enhanced Machine Learning Estimators for ClassificationCode0
A Review on Discriminative Self-supervised Learning Methods in Computer Vision—0
Large Language Models for Cyber Security: A Systematic Literature Review—0
Hypergraph-enhanced Dual Semi-supervised Graph Classification—0
Imagine Flash: Accelerating Emu Diffusion Models with Backward Distillation—0
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance LearningCode0
SingIt! Singer Voice Transformation—0
Enriched BERT Embeddings for Scholarly Publication ClassificationCode0
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
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
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
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer—0
Stable Diffusion Dataset Generation for Downstream Classification Tasks—0
Few-Shot Fruit Segmentation via Transfer LearningCode0
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
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
TartuNLP at EvaLatin 2024: Emotion Polarity Detection—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