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

Showing 19261950 of 10307 papers

TitleStatusHype
Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer LearningCode0
HANA: A HAndwritten NAme Database for Offline Handwritten Text RecognitionCode0
Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulationsCode0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of ActionsCode0
Hardware Conditioned Policies for Multi-Robot Transfer LearningCode0
Hacking Task Confounder in Meta-LearningCode0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
Artificial Color Constancy via GoogLeNet with Angular Loss FunctionCode0
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language ModelsCode0
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationCode0
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publicationsCode0
Learn Continually, Generalize Rapidly: Lifelong Knowledge Accumulation for Few-shot LearningCode0
GTNet: Generative Transfer Network for Zero-Shot Object DetectionCode0
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
A Framework of Transfer Learning in Object Detection for Embedded SystemsCode0
Lightweight and Robust Representation of Economic Scales from Satellite ImageryCode0
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object ClassificationCode0
Growing Neural Network with Shared ParameterCode0
AfriVEC: Word Embedding Models for African Languages. Case Study of Fon and NobiinCode0
Guided Transfer LearningCode0
CADE: Cosine Annealing Differential Evolution for Spiking Neural NetworkCode0
Group-level Emotion Recognition using Transfer Learning from Face IdentificationCode0
GVdoc: Graph-based Visual Document ClassificationCode0
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2Unverified
2DFA-ENTAccuracy69.2Unverified
3DFA-SAFNAccuracy69.1Unverified
4EasyTLAccuracy63.3Unverified
5MEDAAccuracy60.3Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95Unverified