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 2351–2375 of 10307 papers

TitleStatusHype
Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding—0
Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals—0
Towards Foundation Models for Critical Care Time Series—0
SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction—0
Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse HistopathologyCode0
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan—0
LAGUNA: LAnguage Guided UNsupervised Adaptation with structured spaces—0
Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation—0
Knowledge Transfer Across Modalities with Natural Language Supervision—0
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning—0
Implementation of Real-Time Lane Detection on Autonomous Mobile Robot—0
Self-Supervised Learning for Ordered Three-Dimensional Structures—0
Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural NetworkCode0
Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras—0
Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data SettingCode0
Personalization of Wearable Sensor-Based Joint Kinematic Estimation Using Computer Vision for Hip Exoskeleton Applications—0
Revised Regularization for Efficient Continual Learning through Correlation-Based Parameter Update in Bayesian Neural Networks—0
Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote SensingCode0
BERT-Based Approach for Automating Course Articulation Matrix Construction with Explainable AICode0
SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation—0
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
Variable Extraction for Model Recovery in Scientific Literature—0
POS-tagging to highlight the skeletal structure of sentencesCode0
Transforming Static Images Using Generative Models for Video Salient Object Detection—0
MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Collaborative Learning—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