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 6701–6750 of 10307 papers

TitleStatusHype
Representations and Strategies for Transferable Machine Learning Models in Chemical Discovery—0
Learning Graphs for Knowledge Transfer With Limited Labels—0
Practical Transferability Estimation for Image Classification Tasks—0
Informative and Consistent Correspondence Mining for Cross-Domain Weakly Supervised Object Detection—0
Cross Modality Knowledge Distillation for Multi-Modal Aerial View Object ClassificationCode0
Memory Oriented Transfer Learning for Semi-Supervised Image Deraining—0
Self-Supervised Learning on 3D Point Clouds by Learning Discrete Generative Models—0
Scalable Differential Privacy With Sparse Network Finetuning—0
Recurrent Stacking of Layers in Neural Networks: An Application to Neural Machine Translation—0
Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced EnsembleCode0
Adversarial Training Helps Transfer Learning via Better Representations—0
Toward Fault Detection in Industrial Welding Processes with Deep Learning and Data Augmentation—0
Amortized Auto-Tuning: Cost-Efficient Bayesian Transfer Optimization for Hyperparameter RecommendationCode0
Frustratingly Easy Transferability Estimation—0
Dual-Teacher Class-Incremental Learning With Data-Free Generative Replay—0
Evolving Image Compositions for Feature Representation Learning—0
A Hands-on Comparison of DNNs for Dialog Separation Using Transfer Learning from Music Source Separation—0
Generating Thermal Human Faces for Physiological Assessment Using Thermal Sensor Auxiliary LabelsCode0
Bilateral Personalized Dialogue Generation with Contrastive LearningCode0
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
User-specific Adaptive Fine-tuning for Cross-domain Recommendations—0
Why Can You Lay Off Heads? Investigating How BERT Heads Transfer—0
Deep Transfer Learning for Brain Magnetic Resonance Image Multi-class Classification—0
Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings—0
Pre-Trained Models: Past, Present and Future—0
HistoTransfer: Understanding Transfer Learning for Histopathology—0
GenSF: Simultaneous Adaptation of Generative Pre-trained Models and Slot FillingCode0
Domain Generalization on Medical Imaging Classification using Episodic Training with Task Augmentation—0
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users—0
Schema-Guided Paradigm for Zero-Shot DialogCode0
CARTL: Cooperative Adversarially-Robust Transfer LearningCode0
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification—0
Efficient Deep Learning Architectures for Fast Identification of Bacterial Strains in Resource-Constrained DevicesCode0
TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS—0
Supervising the Transfer of Reasoning Patterns in VQA—0
Balanced End-to-End Monolingual pre-training for Low-Resourced Indic Languages Code-Switching Speech Recognition—0
A multi-objective perspective on jointly tuning hardware and hyperparameters—0
Audiovisual transfer learning for audio tagging and sound event detection—0
Low-Dimensional Structure in the Space of Language Representations is Reflected in Brain ResponsesCode0
Probing transfer learning with a model of synthetic correlated datasets—0
AutoFT: Automatic Fine-Tune for Parameters Transfer Learning in Click-Through Rate Prediction—0
Neural Supervised Domain Adaptation by Augmenting Pre-trained Models with Random Units—0
Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data—0
SpaceMeshLab: Spatial Context Memoization and Meshgrid Atrous Convolution Consensus for Semantic Segmentation—0
Predicting the Success of Domain Adaptation in Text Similarity—0
Adaptive transfer learning—0
A Deep Value-network Based Approach for Multi-Driver Order Dispatching—0
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
GAN Cocktail: mixing GANs without dataset accessCode0
LAWDR: Language-Agnostic Weighted Document Representations from Pre-trained Models—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