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 7801–7850 of 10307 papers

TitleStatusHype
Towards Scheduling Federated Deep Learning using Meta-Gradients for Inter-Hospital Learning—0
Towards Single-phase Single-stage Detection of Pulmonary Nodules in Chest CT Imaging—0
Towards species' classification of the Anastrepha pseudoparallela group—0
Towards Sustainable Census Independent Population Estimation in Mozambique—0
Towards Sustainable Personalized On-Device Human Activity Recognition with TinyML and Cloud-Enabled Auto Deployment—0
Towards Task-Prioritized Policy Composition—0
Towards the Detection of Building Occupancy with Synthetic Environmental Data—0
Towards the extraction of robust sign embeddings for low resource sign language recognition—0
Towards the First Machine Translation System for Sumerian Transliterations—0
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains—0
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations—0
Towards Transfer Learning for End-to-End Speech Synthesis from Deep Pre-Trained Language Models—0
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines—0
Towards Trustworthy Unsupervised Domain Adaptation: A Representation Learning Perspective for Enhancing Robustness, Discrimination, and Generalization—0
Towards Unbiased Training in Federated Open-world Semi-supervised Learning—0
Towards Understanding Knowledge Distillation—0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Towards Understanding the Effect of Pretraining Label Granularity—0
Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer—0
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer—0
Towards Unsupervised Domain Adaptation via Domain-Transformer—0
Towards Using Diachronic Distributed Word Representations as Models of Lexical Development—0
Towards Zero-Shot Knowledge Distillation for Natural Language Processing—0
Towards Zero-shot Sign Language Recognition—0
Toxicity Classification in Ukrainian—0
TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models—0
TRAC-1 Shared Task on Aggression Identification: IIT(ISM)@COLING'18—0
DeepTaster: Adversarial Perturbation-Based Fingerprinting to Identify Proprietary Dataset Use in Deep Neural Networks—0
Tracking Universal Features Through Fine-Tuning and Model Merging—0
Traffic control using intelligent timing of traffic lights with reinforcement learning technique and real-time processing of surveillance camera images—0
Traffic Event Detection as a Slot Filling Problem—0
Traffic Flow Estimation using LTE Radio Frequency Counters and Machine Learning—0
Traffic Prediction with Transfer Learning: A Mutual Information-based Approach—0
The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale—0
Traffic Signs Detection and Recognition System using Deep Learning—0
Trained Model Fusion for Object Detection using Gating Network—0
Training a Binary Weight Object Detector by Knowledge Transfer for Autonomous Driving—0
Training A Neural Network For Partially Occluded Road Sign Identification In The Context Of Autonomous Vehicles—0
Training Compact Models for Low Resource Entity Tagging using Pre-trained Language Models—0
Training Data Augmentation for Low-Resource Morphological Inflection—0
Targeted transfer learning to improve performance in small medical physics datasets—0
Training general representations for remote sensing using in-domain knowledge—0
Training multi-objective/multi-task collocation physics-informed neural network with student/teachers transfer learnings—0
Towards Reusable Network Components by Learning Compatible Representations—0
Training Novices: The Role of Human-AI Collaboration and Knowledge Transfer—0
Training on the test set? An analysis of Spampinato et al. [arXiv:1609.00344]—0
Training Robots without Robots: Deep Imitation Learning for Master-to-Robot Policy Transfer—0
Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer—0
Training Spatial-Frequency Visual Prompts and Probabilistic Clusters for Accurate Black-Box Transfer Learning—0
"Train one, Classify one, Teach one" -- Cross-surgery transfer learning for surgical step recognition—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