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 4051–4075 of 10307 papers

TitleStatusHype
Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification—0
A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis—0
Few-Shot Object Detection via Knowledge Transfer—0
Few-Shot Object Detection with Sparse Context Transformers—0
Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AI—0
Class-Specific Channel Attention for Few-Shot Learning—0
Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks—0
Estimating State of Charge for xEV batteries using 1D Convolutional Neural Networks and Transfer Learning—0
Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets—0
Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware—0
A privacy-preserving data storage and service framework based on deep learning and blockchain for construction workers' wearable IoT sensors—0
Few-shot Unified Question Answering: Tuning Models or Prompts?—0
Adversary ML Resilience in Autonomous Driving Through Human Centered Perception Mechanisms—0
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary—0
Active Learning with Transfer Learning—0
Estimating Q(s,s') with Deterministic Dynamics Gradients—0
Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies—0
Estimating Posterior Ratio for Classification: Transfer Learning from Probabilistic Perspective—0
Class Relationship Embedded Learning for Source-Free Unsupervised Domain Adaptation—0
Filtered Inner Product Projection for Crosslingual Embedding Alignment—0
A Prior Knowledge Based Tumor and Tumoral Subregion Segmentation Tool for Pediatric Brain Tumors—0
Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning—0
Financial Aspect-Based Sentiment Analysis using Deep Representations—0
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering—0
Estimating Bicycle Route Attractivity from Image Data—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