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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2626–2650 of 10307 papers

TitleStatusHype
Context-Aware Predictive Coding: A Representation Learning Framework for WiFi SensingCode0
On the Generalizability of Foundation Models for Crop Type MappingCode0
Comparative Analysis of Pretrained Audio Representations in Music Recommender SystemsCode0
Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine InterfacesCode0
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain AdaptationCode0
Using The Concept Hierarchy for Household Action Recognition—0
Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages—0
TheraGen: Therapy for Every Generation—0
Learn from Balance: Rectifying Knowledge Transfer for Long-Tailed Scenarios—0
Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities—0
SPARK: Self-supervised Personalized Real-time Monocular Face Capture—0
DreamBeast: Distilling 3D Fantastical Animals with Part-Aware Knowledge Transfer—0
DVS: Blood cancer detection using novel CNN-based ensemble approach—0
Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learning—0
Music auto-tagging in the long tail: A few-shot approach—0
Identification of head impact locations, speeds, and force based on head kinematicsCode0
Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review—0
Manifold Learning via Foliations and Knowledge Transfer—0
Deep Neural Network-Based Sign Language Recognition: A Comprehensive Approach Using Transfer Learning with Explainability—0
Distributed Convolutional Neural Network Training on Mobile and Edge Clusters—0
Inference is All You Need: Self Example Retriever for Cross-domain Dialogue State Tracking with ChatGPT—0
A Bayesian Framework for Active Tactile Object Recognition, Pose Estimation and Shape Transfer Learning—0
A comprehensive study on Blood Cancer detection and classification using Convolutional Neural Network—0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
Adaptive Meta-Domain Transfer Learning (AMDTL): A Novel Approach for Knowledge Transfer in AICode0
Show:102550
← PrevPage 106 of 413Next →
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