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 3276–3300 of 10307 papers

TitleStatusHype
Stock and market index prediction using Informer network—0
Regularization Through Simultaneous Learning: A Case Study on Plant Classification—0
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization mattersCode1
Transferring Fairness using Multi-Task Learning with Limited Demographic Information—0
Sequential Transfer Learning to Decode Heard and Imagined Timbre from fMRI Data—0
Feasibility of Transfer Learning: A Mathematical Framework—0
Lion: Adversarial Distillation of Proprietary Large Language ModelsCode2
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond—0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
Denoised Self-Augmented Learning for Social RecommendationCode1
Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis—0
LEAN: Light and Efficient Audio Classification Network—0
MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta LearningCode1
An Optimized Ensemble Deep Learning Model For Brain Tumor Classification—0
Strategy Extraction in Single-Agent Games—0
Explaining Emergent In-Context Learning as Kernel Regression—0
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification GraphsCode0
Many or Few Samples? Comparing Transfer, Contrastive and Meta-Learning in Encrypted Traffic Classification—0
CNN-based Methods for Object Recognition with High-Resolution Tactile SensorsCode0
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning—0
PTGB: Pre-Train Graph Neural Networks for Brain Network AnalysisCode1
Self-supervised representations in speech-based depression detection—0
Self-Distillation with Meta Learning for Knowledge Graph CompletionCode0
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor SearchCode1
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning—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