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 3301–3325 of 10307 papers

TitleStatusHype
ECG-CL: A Comprehensive Electrocardiogram Interpretation Method Based on Continual Learning—0
Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ—0
ECG Heartbeat classification using deep transfer learning with Convolutional Neural Network and STFT technique—0
EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation—0
ECLeKTic: a Novel Challenge Set for Evaluation of Cross-Lingual Knowledge Transfer—0
EventBind: Learning a Unified Representation to Bind Them All for Event-based Open-world Understanding—0
Automatic phantom test pattern classification through transfer learning with deep neural networks—0
Attention-Enhanced Prioritized Proximal Policy Optimization for Adaptive Edge Caching—0
Edge Caching Optimization with PPO and Transfer Learning for Dynamic Environments—0
Edge-cloud Collaborative Learning with Federated and Centralized Features—0
AdarGCN: Adaptive Aggregation GCN for Few-Shot Learning—0
Edinburgh’s End-to-End Multilingual Speech Translation System for IWSLT 2021—0
6th Place Solution to Google Universal Image Embedding—0
Edit Once, Update Everywhere: A Simple Framework for Cross-Lingual Knowledge Synchronization in LLMs—0
Edit Transfer: Learning Image Editing via Vision In-Context Relations—0
EEG-based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and their Applications—0
EEG-based Classification of Drivers Attention using Convolutional Neural Network—0
EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning—0
EEG-Based Mental Imagery Task Adaptation via Ensemble of Weight-Decomposed Low-Rank Adapters—0
Enriching a Fashion Knowledge Graph from Product Textual Descriptions—0
EEG Decoding for Datasets with Heterogenous Electrode Configurations using Transfer Learning Graph Neural Networks—0
EEG-NeXt: A Modernized ConvNet for The Classification of Cognitive Activity from EEG—0
EEGPT: Unleashing the Potential of EEG Generalist Foundation Model by Autoregressive Pre-training—0
Self-Distillation Prototypes Network: Learning Robust Speaker Representations without Supervision—0
Deep Job Understanding at LinkedIn—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