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 4151–4200 of 10307 papers

TitleStatusHype
Deep LSTM Spoken Term Detection using Wav2Vec 2.0 Recognizer—0
Generalizing over Long Tail Concepts for Medical Term NormalizationCode0
A Complete Recipe for Bayesian Knowledge Transfer: Object Tracking—0
Super-Resolution and Image Re-projection for Iris Recognition—0
Surgical Fine-Tuning Improves Adaptation to Distribution ShiftsCode1
Context-driven Visual Object Recognition based on Knowledge Graphs—0
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
LAVA: Label-efficient Visual Learning and AdaptationCode0
Transfer learning with affine model transformationCode0
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters—0
PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction—0
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer LearningCode0
Exclusive Supermask Subnetwork Training for Continual LearningCode0
Maestro-U: Leveraging joint speech-text representation learning for zero supervised speech ASR—0
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material ClassificationCode0
Weakly Supervised Learning with Automated Labels from Radiology Reports for Glioma Change Detection—0
On effects of Knowledge Distillation on Transfer Learning—0
Transfer-learning for video classification: Video Swin Transformer on multiple domains—0
BIOWISH: Biometric Recognition using Wearable Inertial Sensors detecting Heart Activity—0
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning—0
Deep Data Augmentation for Weed Recognition Enhancement: A Diffusion Probabilistic Model and Transfer Learning Based ApproachCode1
Conditional Neural Processes for Molecules—0
Review Learning: Alleviating Catastrophic Forgetting with Generative Replay without Generator—0
6th Place Solution to Google Universal Image Embedding—0
A Transfer Learning Based Approach for Classification of COVID-19 and Pneumonia in CT Scan Imaging—0
Accelerating Transfer Learning with Near-Data Computation on Cloud Object StoresCode0
Aplicación de redes neuronales convolucionales profundas al diagnóstico asistido de la enfermedad de AlzheimerCode0
Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material PropertiesCode0
Improving Transfer Learning with a Dual Image and Video Transformer for Multi-label Movie Trailer Genre ClassificationCode0
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRICode0
Is synthetic data from generative models ready for image recognition?Code1
NSCGCN: A novel deep GCN model to diagnosis COVID-19—0
Multi-Task Learning for Joint Semantic Role and Proto-Role Labeling—0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets—0
Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sources—0
Unified Vision and Language Prompt LearningCode1
Cross-dataset COVID-19 Transfer Learning with Cough Detection, Cough Segmentation, and Data AugmentationCode0
Federated Continual Learning for Text Classification via Selective Inter-client TransferCode0
Task Compass: Scaling Multi-task Pre-training with Task PrefixCode1
Text-Derived Knowledge Helps Vision: A Simple Cross-modal Distillation for Video-based Action AnticipationCode0
Token-Label Alignment for Vision TransformersCode1
Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided DecodingCode0
Investigating Massive Multilingual Pre-Trained Machine Translation Models for Clinical Domain via Transfer Learning—0
Prompt Generation Networks for Input-Space Adaptation of Frozen Vision TransformersCode1
Self-supervised video pretraining yields robust and more human-aligned visual representations—0
CLIP also Understands Text: Prompting CLIP for Phrase Understanding—0
The Role of Exploration for Task Transfer in Reinforcement Learning—0
Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment AnalysisCode1
Combining datasets to increase the number of samples and improve model fittingCode0
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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