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 1901–1925 of 10307 papers

TitleStatusHype
Event Extraction in Basque: Typologically motivated Cross-Lingual Transfer-Learning Analysis—0
The impact of data set similarity and diversity on transfer learning success in time series forecasting—0
Supervised Gradual Machine Learning for Aspect Category Detection—0
BatSort: Enhanced Battery Classification with Transfer Learning for Battery Sorting and RecyclingCode0
MULTIFLOW: Shifting Towards Task-Agnostic Vision-Language PruningCode1
DinoBloom: A Foundation Model for Generalizable Cell Embeddings in HematologyCode2
Joint Identifiability of Cross-Domain Recommendation via Hierarchical Subspace Disentanglement—0
Latent-based Diffusion Model for Long-tailed RecognitionCode1
Enhancing Breast Cancer Diagnosis in Mammography: Evaluation and Integration of Convolutional Neural Networks and Explainable AI—0
VoltaVision: A Transfer Learning model for electronic component classificationCode0
Teaching Llama a New Language Through Cross-Lingual Knowledge TransferCode0
How does Multi-Task Training Affect Transformer In-Context Capabilities? Investigations with Function ClassesCode0
Cross-Architecture Transfer Learning for Linear-Cost Inference Transformers—0
Lifelong Event Detection with Embedding Space Separation and Compaction—0
ANGOFA: Leveraging OFA Embedding Initialization and Synthetic Data for Angolan Language ModelCode0
What Are We Measuring When We Evaluate Large Vision-Language Models? An Analysis of Latent Factors and BiasesCode0
Transfer learning applications for anomaly detection in wind turbines—0
Federated Distillation: A Survey—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Disentangled Pre-training for Human-Object Interaction DetectionCode1
Transfer Learning from Whisper for Microscopic Intelligibility Prediction—0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Ukrainian Texts Classification: Exploration of Cross-lingual Knowledge Transfer Approaches—0
ImageNot: A contrast with ImageNet preserves model rankingsCode0
R^2-Tuning: Efficient Image-to-Video Transfer Learning for Video Temporal Grounding—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