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 3426–3450 of 10307 papers

TitleStatusHype
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