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 3401–3450 of 10307 papers

TitleStatusHype
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space—0
Deep Feature Rotation for Multimodal Image Style Transfer—0
Efficient transfer learning and online adaptation with latent variable models for continuous control—0
Deep Feature Learning for Graphs—0
Efficient Transfer Learning for Quality Estimation with Bottleneck Adapter Layer—0
An Uncertainty-Aware Deep Learning Framework for Defect Detection in Casting Products—0
An Acceleration Method Based on Deep Learning and Multilinear Feature Space—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
Efficient Transfer Learning Schemes for Personalized Language Modeling using Recurrent Neural Network—0
Efficient Transfer Learning via Joint Adaptation of Network Architecture and Weight—0
How to improve CNN-based 6-DoF camera pose estimation—0
Deepfake Video Forensics based on Transfer Learning—0
Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks—0
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario—0
Deepfake Detection of Face Images based on a Convolutional Neural Network—0
Causal Time-Series Synchronization for Multi-Dimensional Forecasting—0
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift—0
Actionable Phrase Detection using NLP—0
Deepfake Audio Detection Using Spectrogram-based Feature and Ensemble of Deep Learning Models—0
ELCC: the Emergent Language Corpus Collection—0
Cause-Effect Preservation and Classification using Neurochaos Learning—0
Electron-nucleus cross sections from transfer learning—0
AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue—0
A Comparison of Few-Shot Learning Methods for Underwater Optical and Sonar Image Classification—0
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System—0
ELiTe: Efficient Image-to-LiDAR Knowledge Transfer for Semantic Segmentation—0
CCS-GAN: COVID-19 CT-scan classification with very few positive training images—0
ELSIM: End-to-end learning of reusable skills through intrinsic motivation—0
CCT-Net: Category-Invariant Cross-Domain Transfer for Medical Single-to-Multiple Disease Diagnosis—0
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network—0
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains—0
Embedding Compression for Teacher-to-Student Knowledge Transfer—0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning—0
Adversarial Feature Training for Generalizable Robotic Visuomotor Control—0
EvoSampling: A Granular Ball-based Evolutionary Hybrid Sampling with Knowledge Transfer for Imbalanced Learning—0
Embeddings models for Buddhist Sanskrit—0
Embed Everything: A Method for Efficiently Co-Embedding Multi-Modal Spaces—0
Embodied Multimodal Multitask Learning—0
Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms—0
CEHR-BERT: Incorporating temporal information from structured EHR data to improve prediction tasks—0
DeepEthnic: Multi-Label Ethnic Classification from Face Images—0
Emerging Trends in Federated Learning: From Model Fusion to Federated X Learning—0
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions—0
Deep Ensembling for Perceptual Image Quality Assessment—0
Pay attention to emoji: Feature Fusion Network with EmoGraph2vec Model for Sentiment Analysis—0
CellCentroidFormer: Combining Self-attention and Convolution for Cell Detection—0
Deep Ensembles for Low-Data Transfer Learning—0
CellLineNet: End-to-End Learning and Transfer Learning For Multiclass Epithelial Breast cell Line Classification via a Convolutional Neural Network—0
Emotion Classification in Low and Moderate Resource Languages—0
Automatic Discovery of Novel Intents & Domains from Text Utterances—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