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 5076–5100 of 10307 papers

TitleStatusHype
Manifold Alignment with Label Information—0
Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning—0
Baby Physical Safety Monitoring in Smart Home Using Action Recognition System—0
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity RecognitionCode0
Probing Transfer in Deep Reinforcement Learning without Task Engineering—0
Tools for Extracting Spatio-Temporal Patterns in Meteorological Image Sequences: From Feature Engineering to Attention-Based Neural Networks—0
Generalizing over Long Tail Concepts for Medical Term NormalizationCode0
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding—0
Real-time Detection of 2D Tool Landmarks with Synthetic Training Data—0
Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer—0
A Complete Recipe for Bayesian Knowledge Transfer: Object Tracking—0
Context-driven Visual Object Recognition based on Knowledge Graphs—0
Super-Resolution and Image Re-projection for Iris Recognition—0
LAVA: Label-efficient Visual Learning and AdaptationCode0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
Time and Cost-Efficient Bathymetric Mapping System using Sparse Point Cloud Generation and Automatic Object DetectionCode0
Transfer-learning for video classification: Video Swin Transformer on multiple domains—0
Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection—0
Transfer learning with affine model transformationCode0
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters—0
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning—0
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer LearningCode0
Exclusive Supermask Subnetwork Training for Continual LearningCode0
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction—0
BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity—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