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 6801–6850 of 10307 papers

TitleStatusHype
Universal Adaptive Control of Nonlinear Systems—0
Towards Zero-Shot Knowledge Distillation for Natural Language Processing—0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
Comparison of different CNNs for breast tumor classification from ultrasound images—0
Screening COVID-19 Based on CT/CXR Images & Building a Publicly Available CT-scan Dataset of COVID-19—0
Ranking and Rejecting of Pre-Trained Deep Neural Networks in Transfer Learning based on Separation Index—0
PaXNet: Dental Caries Detection in Panoramic X-ray using Ensemble Transfer Learning and Capsule Classifier—0
Towards a Universal Continuous Knowledge Base—0
Seed Phenotyping on Neural Networks using Domain Randomization and Transfer Learning—0
Evolutionary Gait Transfer of Multi-Legged Robots in Complex Terrains—0
Cross-lingual Universal Dependency Parsing Only from One Monolingual Treebank—0
Decentralized Federated Learning via Mutual Knowledge Transfer—0
Diabetic Retinopathy Grading System Based on Transfer Learning—0
Learning Dense Representations of Phrases at ScaleCode2
A Review of Artificial Intelligence Technologies for Early Prediction of Alzheimer's Disease—0
Flexible deep transfer learning by separate feature embeddings and manifold alignment—0
Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-TuningCode1
MailLeak: Obfuscation-Robust Character Extraction Using Transfer Learning—0
Objective Evaluation of Deep Uncertainty Predictions for COVID-19 Detection—0
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition—0
Unboxing Engagement in YouTube Influencer Videos: An Attention-Based Approach—0
Training Convolutional Neural Networks With Hebbian Principal Component AnalysisCode1
Cross-Domain Latent Modulation for Variational Transfer Learning—0
Image Translation via Fine-grained Knowledge TransferCode1
Knowledge Transfer Based Fine-grained Visual ClassificationCode0
Domain-adaptive Fall Detection Using Deep Adversarial Training—0
Breaking Writer's Block: Low-cost Fine-tuning of Natural Language Generation Models—0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Deep learning and high harmonic generation—0
Multimodal Transfer Learning-based Approaches for Retinal Vascular Segmentation—0
Transfer Learning Based Automatic Model Creation Tool For Resource Constraint Devices—0
Computation-Efficient Knowledge Distillation via Uncertainty-Aware MixupCode1
FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware ModellingCode1
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—0
Learning Cross-Domain Correspondence for Control with Dynamics Cycle-ConsistencyCode1
Multi-Modal Depth Estimation Using Convolutional Neural Networks—0
The effectiveness of unsupervised subword modeling with autoregressive and cross-lingual phone-aware networks—0
ISD: Self-Supervised Learning by Iterative Similarity DistillationCode1
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning—0
Multilingual Evidence Retrieval and Fact Verification to Combat Global Disinformation: The Power of PolyglotismCode0
Trex: Learning Execution Semantics from Micro-Traces for Binary SimilarityCode1
Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Images—0
Wasserstein Contrastive Representation Distillation—0
Deep Learning Based Classification of Unsegmented Phonocardiogram Spectrograms Leveraging Transfer Learning—0
Learning Prediction Intervals for Model Performance—0
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interactionCode0
Exploring Transfer Learning For End-to-End Spoken Language Understanding—0
Hypothesis Disparity Regularized Mutual Information Maximization—0
Cluster, Split, Fuse, and Update: Meta-Learning for Open Compound Domain Adaptive Semantic Segmentation—0
DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection—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