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 1951–2000 of 10307 papers

TitleStatusHype
ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning—0
Commonsense Knowledge Transfer for Pre-trained Language Models—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Char-RNN for Word Stress Detection in East Slavic Languages—0
A Physics-driven GraphSAGE Method for Physical Process Simulations Described by Partial Differential Equations—0
Cross-Environment Transfer Learning for Location-Aided Beam Prediction in 5G and Beyond Millimeter-Wave Networks—0
Comparative Analysis of Deep Learning Models for Crop Disease Detection: A Transfer Learning Approach—0
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos—0
Cross-functional transferability in universal machine learning interatomic potentials—0
Comparative Analysis of Lightweight Deep Learning Models for Memory-Constrained Devices—0
Comparative Analysis of Modality Fusion Approaches for Audio-Visual Person Identification and Verification—0
A Scenario-Based Functional Testing Approach to Improving DNN Performance—0
Cross-Lingual Transfer Learning for Statistical Type Inference—0
Comparative Analysis of Transfer Learning in Deep Learning Text-to-Speech Models on a Few-Shot, Low-Resource, Customized Dataset—0
A scoping review of transfer learning research on medical image analysis using ImageNet—0
Crowd Sourcing based Active Learning Approach for Parking Sign Recognition—0
Characterizing and Understanding the Generalization Error of Transfer Learning with Gibbs Algorithm—0
Characterizing and Avoiding Negative Transfer—0
A Segmentation Foundation Model for Diverse-type Tumors—0
Comparing Male Nyala and Male Kudu Classification using Transfer Learning with ResNet-50 and VGG-16—0
Comparing Transfer and Meta Learning Approaches on a Unified Few-Shot Classification Benchmark—0
Comparing Unsupervised Word Translation Methods Step by Step—0
Comparison of Deep Learning Approaches for Multi-Label Chest X-Ray Classification—0
Comparison of different CNNs for breast tumor classification from ultrasound images—0
Comparison of fine-tuning strategies for transfer learning in medical image classification—0
Comparison of Neural Models for X-ray Image Classification in COVID-19 Detection—0
Comparison of self-supervised in-domain and supervised out-domain transfer learning for bird species recognition—0
Comparison of Semantic Segmentation Approaches for Horizon/Sky Line Detection—0
Comparison of semi-supervised learning methods for High Content Screening quality control—0
Comparison of Transfer Learning based Additive Manufacturing Models via A Case Study—0
Compiler Provenance Recovery for Multi-CPU Architectures Using a Centrifuge Mechanism—0
Monocular Cyclist Detection with Convolutional Neural Networks—0
The (In)Effectiveness of Intermediate Task Training For Domain Adaptation and Cross-Lingual Transfer Learning—0
A Sequence Matching Network for Polyphonic Sound Event Localization and Detection—0
Complying with the EU AI Act: Innovations in Explainable and User-Centric Hand Gesture Recognition—0
A physics-based domain adaptation framework for modelling and forecasting building energy systems—0
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition—0
Channel-wise pruning of neural networks with tapering resource constraint—0
Composable Sparse Fine-Tuning for Cross-Lingual Transfer—0
Composing Task-Agnostic Policies with Deep Reinforcement Learning—0
Time-Frequency Analysis based Deep Interference Classification for Frequency Hopping System—0
A Petri Dish for Histopathology Image Analysis—0
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks—0
Compositional Zero-Shot Domain Transfer with Text-to-Text Models—0
Channel Scaling: A Scale-and-Select Approach for Transfer Learning—0
Change your singer: a transfer learning generative adversarial framework for song to song conversion—0
A Permutation-Invariant Representation of Neural Networks with Neuron Embeddings—0
Comprehensive performance comparison among different types of features in data-driven battery state of health estimation—0
Action Recognition for American Sign Language—0
Assessing the Performance of Analog Training for Transfer Learning—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