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 6301–6350 of 10307 papers

TitleStatusHype
Learning Cooperation and Online Planning Through Simulation and Graph Convolutional Network—0
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora—0
A Variational Bayesian Approach to Learning Latent Variables for Acoustic Knowledge TransferCode0
An Acceleration Method Based on Deep Learning and Multilinear Feature Space—0
Dataset Knowledge Transfer for Class-Incremental Learning without MemoryCode0
Knowledge Inheritance for Pre-trained Language Models—0
Evaluation of Transfer Learning for Polish with a text-to-text model—0
Simplest Streaming TreesCode0
Towards Using Diachronic Distributed Word Representations as Models of Lexical Development—0
Benchmark Problems for CEC2021 Competition on Evolutionary Transfer Multiobjectve OptimizationCode0
SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer—0
Multilingual Speech Recognition using Knowledge Transfer across Learning Processes—0
Scribosermo: Fast Speech-to-Text models for German and other LanguagesCode0
Automatic Detection of COVID-19 and Pneumonia from Chest X-Ray using Deep Learning—0
Improving Hyperparameter Optimization by Planning Ahead—0
Region Semantically Aligned Network for Zero-Shot Learning—0
Omni-Training: Bridging Pre-Training and Meta-Training for Few-Shot Learning—0
Domain generalization in deep learning for contrast-enhanced imaging—0
Finding Materialized Models for Model ReuseCode0
MedNet: Pre-trained Convolutional Neural Network Model for the Medical Imaging Tasks—0
Winning the ICCV'2021 VALUE Challenge: Task-aware Ensemble and Transfer Learning with Visual Concepts—0
Småprat: DialoGPT for Natural Language Generation of Swedish Dialogue by Transfer Learning—0
Detecting Damage Building Using Real-time Crowdsourced Images and Transfer LearningCode0
Swish-Driven GoogleNet for Intelligent Analog Beam Selection in Terahertz Beamspace MIMOCode0
Deep Learning for Bias Detection: From Inception to Deployment—0
Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells—0
Adapting TTS models For New Speakers using Transfer Learning—0
Investigating Transfer Learning Capabilities of Vision Transformers and CNNs by Fine-Tuning a Single Trainable BlockCode0
Leveraging Transformers for StarCraft Macromanagement Prediction—0
Cross Domain Emotion Recognition using Few Shot Knowledge Transfer—0
Machine Learning for the Control and Monitoring of Electric Machine Drives: Advances and Trends—0
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
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces—0
Self-adaptive Multi-task Particle Swarm Optimization—0
Combining Image Features and Patient Metadata to Enhance Transfer Learning—0
VieSum: How Robust Are Transformer-based Models on Vietnamese Summarization?—0
Conceptual Expansion Neural Architecture Search (CENAS)—0
Magic dust for cross-lingual adaptation of monolingual wav2vec-2.0—0
BadPre: Task-agnostic Backdoor Attacks to Pre-trained NLP Foundation Models—0
The Power of Contrast for Feature Learning: A Theoretical Analysis—0
On The Transferability of Deep-Q Networks—0
A Survey of Fish Tracking Techniques Based on Computer Vision—0
LIDSNet: A Lightweight on-device Intent Detection model using Deep Siamese Network—0
Sequential Reptile: Inter-Task Gradient Alignment for Multilingual Learning—0
WHO-Hand Hygiene Gesture Classification System—0
Voice Aging with Audio-Visual Style Transfer—0
Deep Subspace analysing for Semi-Supervised multi-label classification of Diabetic Foot Ulcer—0
Deep reinforcement learning for guidewire navigation in coronary artery phantom—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