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 326–350 of 10307 papers

TitleStatusHype
Detecting Glioma, Meningioma, and Pituitary Tumors, and Normal Brain Tissues based on Yolov11 and Yolov8 Deep Learning Models—0
From Colors to Classes: Emergence of Concepts in Vision TransformersCode0
Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation—0
Crossmodal Knowledge Distillation with WordNet-Relaxed Text Embeddings for Robust Image Classification—0
Advanced Deep Learning and Large Language Models: Comprehensive Insights for Cancer Detection—0
A QUBO Framework for Team Formation—0
Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learningCode0
A Survey on Remote Sensing Foundation Models: From Vision to MultimodalityCode2
Nonhuman Primate Brain Tissue Segmentation Using a Transfer Learning Approach—0
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning—0
Beyond Vanilla Fine-Tuning: Leveraging Multistage, Multilingual, and Domain-Specific Methods for Low-Resource Machine Translation—0
Masked Self-Supervised Pre-Training for Text Recognition Transformers on Large-Scale Datasets—0
On-site estimation of battery electrochemical parameters via transfer learning based physics-informed neural network approach—0
A Multi-Modal Knowledge-Enhanced Framework for Vessel Trajectory Prediction—0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
A Theoretical Analysis of Analogy-Based Evolutionary Transfer Optimization—0
Low-Resource Transliteration for Roman-Urdu and Urdu Using Transformer-Based Models—0
Residual Learning Inspired Crossover Operator and Strategy Enhancements for Evolutionary Multitasking—0
JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models' Detection of Human Self-Destructive Behavior Content in Jirai Community—0
Exploring the flavor structure of leptons via diffusion models—0
World Model Agents with Change-Based Intrinsic MotivationCode0
Small Object Detection: A Comprehensive Survey on Challenges, Techniques and Real-World Applications—0
Multi-dataset and Transfer Learning Using Gene Expression Knowledge GraphsCode0
Low-resource Information Extraction with the European Clinical Case Corpus—0
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis—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