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 6201–6225 of 10307 papers

TitleStatusHype
Programmable Neural Network Trojan for Pre-Trained Feature Extractor—0
Program-to-Circuit: Exploiting GNNs for Program Representation and Circuit Translation—0
Progressive Class-level Distillation—0
Balanced Multi-modal Federated Learning via Cross-Modal Infiltration—0
Progressive Joint Modeling in Unsupervised Single-channel Overlapped Speech Recognition—0
Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds—0
Balanced Semi-Supervised Generative Adversarial Network for Damage Assessment from Low-Data Imbalanced-Class Regime—0
Progressive reduced order modeling: empowering data-driven modeling with selective knowledge transfer—0
Progressive Reinforcement Learning with Distillation for Multi-Skilled Motion Control—0
Balancing Accuracy and Training Time in Federated Learning for Violence Detection in Surveillance Videos: A Study of Neural Network Architectures—0
Progressive trajectory matching for medical dataset distillation—0
Progressive transfer learning for low frequency data prediction in full waveform inversion—0
Progressive Transfer Learning for Multi-Pass Fundus Image Restoration—0
Balancing Data through Data Augmentation Improves the Generality of Transfer Learning for Diabetic Retinopathy Classification—0
Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features—0
Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research—0
Balancing Exploration and Exploitation in LLM using Soft RLLF for Enhanced Negation Understanding—0
BANANA at WNUT-2020 Task 2: Identifying COVID-19 Information on Twitter by Combining Deep Learning and Transfer Learning Models—0
Prompted Meta-Learning for Few-shot Knowledge Graph Completion—0
Banana Ripeness Level Classification using a Simple CNN Model Trained with Real and Synthetic Datasets—0
Prompting Large Language Model for Machine Translation: A Case Study—0
Banana Sub-Family Classification and Quality Prediction using Computer Vision—0
ActivityCLIP: Enhancing Group Activity Recognition by Mining Complementary Information from Text to Supplement Image Modality—0
Propagation-aware Social Recommendation by Transfer Learning—0
Proper Reuse of Image Classification Features Improves Object Detection—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