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 3426–3450 of 10307 papers

TitleStatusHype
Improving Speech Translation by Cross-Modal Multi-Grained Contrastive Learning—0
Independent Feature Decomposition and Instance Alignment for Unsupervised Domain AdaptationCode0
Knowledge Distillation Under Ideal Joint Classifier Assumption—0
Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite ImageryCode0
Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review—0
Analyzing the Domain Shift Immunity of Deep Homography EstimationCode0
UniCal: a Single-Branch Transformer-Based Model for Camera-to-LiDAR Calibration and Validation—0
Cashew dataset generation using augmentation and RaLSGAN and a transfer learning based tinyML approach towards disease detection—0
Performance of GAN-based augmentation for deep learning COVID-19 image classificationCode0
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization—0
RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario UnderstandingCode1
CyFormer: Accurate State-of-Health Prediction of Lithium-Ion Batteries via Cyclic Attention—0
360^ High-Resolution Depth Estimation via Uncertainty-aware Structural Knowledge Transfer—0
MisRoBÆRTa: Transformers versus MisinformationCode0
Neural Machine Translation For Low Resource LanguagesCode0
PBNR: Prompt-based News Recommender System—0
Generating an interactive online map of future sea level rise along the North Shore of Vancouver: methods and insights on enabling geovisualisation for coastal communities—0
Exploring Incompatible Knowledge Transfer in Few-shot Image GenerationCode1
Context-aware Domain Adaptation for Time Series Anomaly Detection—0
MVP-SEG: Multi-View Prompt Learning for Open-Vocabulary Semantic Segmentation—0
Symbiotic Message Passing Model for Transfer Learning between Anti-Fungal and Anti-Bacterial Domains—0
Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator—0
How Will It Drape Like? Capturing Fabric Mechanics from Depth Images—0
SpectFormer: Frequency and Attention is what you need in a Vision Transformer—0
Edge-cloud Collaborative Learning with Federated and Centralized Features—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