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 1451–1475 of 10307 papers

TitleStatusHype
Cross-Phase Mutual Learning Framework for Pulmonary Embolism Identification on Non-Contrast CT Scans—0
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process—0
Exploration in Knowledge Transfer Utilizing Reinforcement Learning—0
Accessing Vision Foundation Models at ImageNet-level CostsCode2
Deep-Learning-Based Markerless Pose Estimation Systems in Gait Analysis: DeepLabCut Custom Training and the Refinement Function—0
Detecting Omissions in Geographic Maps through Computer VisionCode1
Order parameters and phase transitions of continual learning in deep neural networks—0
FSD-BEV: Foreground Self-Distillation for Multi-view 3D Object DetectionCode1
Automated detection of gibbon calls from passive acoustic monitoring data using convolutional neural networks in the "torch for R" ecosystem—0
Combining Federated Learning and Control: A Survey—0
Tissue-Contrastive Semi-Masked Autoencoders for Segmentation Pretraining on Chest CT—0
Exploring Knowledge Transfer in Evolutionary Many-task Optimization: A Complex Network Perspective—0
Domain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification—0
A Cantor-Kantorovich Metric Between Markov Decision Processes with Application to Transfer Learning—0
Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles—0
AddressCLIP: Empowering Vision-Language Models for City-wide Image Address LocalizationCode2
Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset—0
How to Make Cross Encoder a Good Teacher for Efficient Image-Text Retrieval?—0
Towards a text-based quantitative and explainable histopathology image analysisCode0
Fine-Grained Classification for Poisonous Fungi Identification with Transfer LearningCode0
SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer LearningCode0
Robust and Explainable Framework to Address Data Scarcity in Diagnostic Imaging—0
Reprogramming Distillation for Medical Foundation ModelsCode0
Rethinking Image-to-Video Adaptation: An Object-centric Perspective—0
Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled ApproachCode0
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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