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 3001–3025 of 10307 papers

TitleStatusHype
UrbanSARFloods: Sentinel-1 SLC-Based Benchmark Dataset for Urban and Open-Area Flood Mapping—0
Step Out and Seek Around: On Warm-Start Training with Incremental Data—0
Continuous Test-time Domain Adaptation for Efficient Fault Detection under Evolving Operating Conditions—0
Transfer Learning for Latent Variable Network Models—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
M2D-CLAP: Masked Modeling Duo Meets CLAP for Learning General-purpose Audio-Language Representation—0
Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs—0
Randomized Geometric Algebra Methods for Convex Neural NetworksCode0
CADE: Cosine Annealing Differential Evolution for Spiking Neural NetworkCode0
Towards Neural Architecture Search for Transfer Learning in 6G Networks—0
Understanding the Cross-Domain Capabilities of Video-Based Few-Shot Action Recognition Models—0
Towards Practical Single-shot Motion Synthesis—0
The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry—0
Hybrid-Learning Video Moment Retrieval across Multi-Domain Labels—0
Multi-Agent Transfer Learning via Temporal Contrastive Learning—0
A Diagnostic Model for Acute Lymphoblastic Leukemia Using Metaheuristics and Deep Learning Methods—0
Diffusion Tuning: Transferring Diffusion Models via Chain of Forgetting—0
Phonetic Error Analysis of Raw Waveform Acoustic Models with Parametric and Non-Parametric CNNs—0
Improvement of Applicability in Student Performance Prediction Based on Transfer Learning—0
Federated and Transfer Learning for Cancer Detection Based on Image Analysis—0
A Machine Learning-Based Framework for Assessing Cryptographic Indistinguishability of Lightweight Block Ciphers—0
GKT: A Novel Guidance-Based Knowledge Transfer Framework For Efficient Cloud-edge Collaboration LLM DeploymentCode0
Disentangling and Mitigating the Impact of Task Similarity for Continual LearningCode0
Efficient Systematic Reviews: Literature Filtering with Transformers & Transfer Learning—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