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 1601–1650 of 10307 papers

TitleStatusHype
QMamba: On First Exploration of Vision Mamba for Image Quality AssessmentCode0
Measuring training variability from stochastic optimization using robust nonparametric testing—0
WMAdapter: Adding WaterMark Control to Latent Diffusion Models—0
Differentially Private Prototypes for Imbalanced Transfer Learning—0
PRIBOOT: A New Data-Driven Expert for Improved Driving SimulationsCode0
Strategies for Pretraining Neural OperatorsCode0
Indoor Fire and Smoke Detection Using Soft-Voting Based Deep Ensemble ModelCode0
Advancing Roadway Sign Detection with YOLO Models and Transfer Learning—0
Unleashing the Power of Transfer Learning Model for Sophisticated Insect Detection: Revolutionizing Insect Classification—0
Large Language Models are Limited in Out-of-Context Knowledge ReasoningCode0
Teaching with Uncertainty: Unleashing the Potential of Knowledge Distillation in Object Detection—0
BertaQA: How Much Do Language Models Know About Local Culture?Code0
Transferring Knowledge from Large Foundation Models to Small Downstream Models—0
Augmenting Offline RL with Unlabeled Data—0
SSCL-IDS: Enhancing Generalization of Intrusion Detection with Self-Supervised Contrastive LearningCode0
SecureNet: A Comparative Study of DeBERTa and Large Language Models for Phishing Detection—0
Network-Based Transfer Learning Helps Improve Short-Term Crime Prediction Accuracy—0
Contrastive learning of T cell receptor representationsCode0
A Statistical Theory of Regularization-Based Continual Learning—0
Utilizing Grounded SAM for self-supervised frugal camouflaged human detection—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models—0
Prioritizing Potential Wetland Areas via Region-to-Region Knowledge Transfer and Adaptive Propagation—0
Concept Formation and Alignment in Language Models: Bridging Statistical Patterns in Latent Space to Concept Taxonomy—0
MSAGPT: Neural Prompting Protein Structure Prediction via MSA Generative Pre-TrainingCode1
DeviceBERT: Applied Transfer Learning With Targeted Annotations and Vocabulary Enrichment to Identify Medical Device and Component Terminology in FDA Recall Summaries—0
Online Policy Distillation with Decision-Attention—0
Accelerating evolutionary exploration through language model-based transfer learning—0
Low-Resource Cross-Lingual Summarization through Few-Shot Learning with Large Language Models—0
FunBO: Discovering Acquisition Functions for Bayesian Optimization with FunSearch—0
Through the Thicket: A Study of Number-Oriented LLMs derived from Random Forest Models—0
Labeled Data Selection for Category Discovery—0
InaGVAD : a Challenging French TV and Radio Corpus Annotated for Speech Activity Detection and Speaker Gender SegmentationCode1
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions—0
LLMEmbed: Rethinking Lightweight LLM's Genuine Function in Text ClassificationCode1
Step Out and Seek Around: On Warm-Start Training with Incremental Data—0
UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping—0
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning—0
Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential RecommendationCode0
Multi-Task Multi-Scale Contrastive Knowledge Distillation for Efficient Medical Image SegmentationCode1
Transfer Learning for Latent Variable Network Models—0
M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation—0
Randomized Geometric Algebra Methods for Convex Neural NetworksCode0
Leveraging Predicate and Triplet Learning for Scene Graph GenerationCode1
Towards Neural Architecture Search for Transfer Learning in 6G Networks—0
CADE: Cosine Annealing Differential Evolution for Spiking Neural NetworkCode0
Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs—0
FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language ModelsCode3
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry—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