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 5701–5750 of 10307 papers

TitleStatusHype
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Investigating Transfer Learning Capabilities of Vision Transformers and CNNs by Fine-Tuning a Single Trainable BlockCode0
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends—0
Cross Domain Emotion Recognition using Few Shot Knowledge Transfer—0
Performance Evaluation of Deep Transfer Learning on Multiclass Identification of Common Weed Species in Cotton Production SystemsCode1
Reinforcement Learning for Systematic FX Trading—0
Hard instance learning for quantum adiabatic prime factorization—0
Unsupervised High-Fidelity Facial Texture Generation and Reconstruction—0
Vector-quantized Image Modeling with Improved VQGANCode1
Self-adaptive Multi-task Particle Swarm Optimization—0
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces—0
Combining Image Features and Patient Metadata to Enhance Transfer Learning—0
Towards a Unified View of Parameter-Efficient Transfer LearningCode1
VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?—0
Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command RecognitionCode1
Conceptual Expansion Neural Architecture Search (CENAS)—0
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0—0
Bridge to Target Domain by Prototypical Contrastive Learning and Label Confusion: Re-explore Zero-Shot Learning for Slot FillingCode1
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network—0
PoNet: Pooling Network for Efficient Token Mixing in Long SequencesCode1
Deep Transfer Learning for Land Use and Land Cover Classification: A Comparative StudyCode1
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning—0
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models—0
KNOT: Knowledge Distillation using Optimal Transport for Solving NLP TasksCode1
A Survey of Fish Tracking Techniques Based on Computer Vision—0
Study on Transfer Learning Capabilities for Pneumonia Classification in Chest-X-Rays ImageCode1
On The Transferability of Deep-Q Networks—0
The Power of Contrast for Feature Learning: A Theoretical Analysis—0
WHO-Hand Hygiene Gesture Classification System—0
Voice Aging with Audio-Visual Style Transfer—0
Transfer Learning U-Net Deep Learning for Lung Ultrasound SegmentationCode1
Hybrid Classical-Quantum method for Diabetic Foot Ulcer Classification—0
Deep Subspace analysing for Semi-Supervised multi-label classification of Diabetic Foot Ulcer—0
Self-Supervised Generative Style Transfer for One-Shot Medical Image SegmentationCode1
Deep reinforcement learning for guidewire navigation in coronary artery phantom—0
MEBeauty: a multi-ethnic facial beauty dataset in-the-wildCode1
Revisiting Self-Training for Few-Shot Learning of Language ModelCode1
Text-based automatic personality prediction: A bibliographic review—0
TinyFedTL: Federated Transfer Learning on Tiny Devices—0
GROWN: GRow Only When Necessary for Continual Learning—0
Attention module improves both performance and interpretability of 4D fMRI decoding neural network—0
Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data—0
Universal Recurrent Neural Network Grammar—0
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics—0
Exploiting Low-Resource Code-Switching Data to Mandarin-English Speech Recognition Systems—0
A Preliminary Study on Environmental Sound Classification Leveraging Large-Scale Pretrained Model and Semi-Supervised Learning—0
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
Breast Cancer Diagnosis in Two-View Mammography Using End-to-End Trained EfficientNet-Based Convolutional NetworkCode1
CCS-GAN: COVID-19 CT-scan classification with very few positive training images—0
Factored couplings in multi-marginal optimal transport via difference of convex programming—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