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 6501–6525 of 10307 papers

TitleStatusHype
Informed Forecasting: Leveraging Auxiliary Knowledge to Boost LLM Performance on Time Series Forecasting—0
InfoSAM: Fine-Tuning the Segment Anything Model from An Information-Theoretic Perspective—0
Application of DenseNet in Camera Model Identification and Post-processing Detection—0
Semi-supervised Domain Adaptation in Graph Transfer Learning—0
A Comprehensive Survey of Multilingual Neural Machine Translation—0
Injecting Prior Knowledge for Transfer Learning into Reinforcement Learning Algorithms using Logic Tensor Networks—0
Semi-Supervised Histology Classification using Deep Multiple Instance Learning and Contrastive Predictive Coding—0
In Neural Machine Translation, What Does Transfer Learning Transfer?—0
Innovative Speech-Based Deep Learning Approaches for Parkinson's Disease Classification: A Systematic Review—0
Application of Artificial Intelligence in the Classification of Microscopical Starch Images for Drug Formulation—0
Terahertz Pulse Shaping Using Diffractive Surfaces—0
In Rain or Shine: Understanding and Overcoming Dataset Bias for Improving Robustness Against Weather Corruptions for Autonomous Vehicles—0
InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking—0
In-Situ Melt Pool Characterization via Thermal Imaging for Defect Detection in Directed Energy Deposition Using Vision Transformers—0
Inspect Transfer Learning Architecture with Dilated Convolution—0
Inspiration Learning through Preferences—0
Instance-based Deep Transfer Learning—0
Instance-based Inductive Deep Transfer Learning by Cross-Dataset Querying with Locality Sensitive Hashing—0
Instance-based Transfer Learning for Multilingual Deep Retrieval—0
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network—0
A Point in the Right Direction: Vector Prediction for Spatially-aware Self-supervised Volumetric Representation Learning—0
Instance-weighted Transfer Learning of Active Appearance Models—0
Attend and Enrich: Enhanced Visual Prompt for Zero-Shot Learning—0
Instruction-Guided Autoregressive Neural Network Parameter Generation—0
Semi-Supervised Learning Approach to Discover Enterprise User Insights from Feedback and Support—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