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 1201–1225 of 10307 papers

TitleStatusHype
On the Generalizability of Foundation Models for Crop Type MappingCode0
Train-On-Request: An On-Device Continual Learning Workflow for Adaptive Real-World Brain Machine InterfacesCode0
Comparative Analysis of Pretrained Audio Representations in Music Recommender SystemsCode0
Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages—0
DELTA: Dual Consistency Delving with Topological Uncertainty for Active Graph Domain AdaptationCode0
Data Efficient Child-Adult Speaker Diarization with Simulated ConversationsCode1
Using The Concept Hierarchy for Household Action Recognition—0
DVS: Blood cancer detection using novel CNN-based ensemble approach—0
TheraGen: Therapy for Every Generation—0
SPARK: Self-supervised Personalized Real-time Monocular Face Capture—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
Music auto-tagging in the long tail: A few-shot approach—0
DreamBeast: Distilling 3D Fantastical Animals with Part-Aware Knowledge Transfer—0
SimMAT: Exploring Transferability from Vision Foundation Models to Any Image ModalityCode1
Reimagining Linear Probing: Kolmogorov-Arnold Networks in Transfer Learning—0
Identification of head impact locations, speeds, and force based on head kinematicsCode0
Distributed Convolutional Neural Network Training on Mobile and Edge Clusters—0
Manifold Learning via Foliations and Knowledge Transfer—0
Deep Learning Techniques for Hand Vein Biometrics: A Comprehensive Review—0
Deep Neural Network-Based Sign Language Recognition: A Comprehensive Approach Using Transfer Learning with Explainability—0
Cross Dataset Analysis and Network Architecture Repair for Autonomous Car Lane Detection—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
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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