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 19011925 of 10307 papers

TitleStatusHype
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
Are we done with object recognition? The iCub robot's perspectiveCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Hardware Conditioned Policies for Multi-Robot Transfer LearningCode0
Learning Time-Sensitive Strategies in Space FortressCode0
Hardware-accelerated Mars Sample Localization via deep transfer learning from photorealistic simulationsCode0
Learning to Generalize Compositionally by Transferring Across Semantic Parsing TasksCode0
Harnessing multiple LLMs for Information Retrieval: A case study on Deep Learning methodologies in Biodiversity publicationsCode0
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of ActionsCode0
Learning to Prompt Knowledge Transfer for Open-World Continual LearningCode0
Can a powerful neural network be a teacher for a weaker neural network?Code0
HANA: A HAndwritten NAme Database for Offline Handwritten Text RecognitionCode0
Advancing Adversarial Suffix Transfer Learning on Aligned Large Language ModelsCode0
HierarchicalContrast: A Coarse-to-Fine Contrastive Learning Framework for Cross-Domain Zero-Shot Slot FillingCode0
Guided Transfer LearningCode0
GVdoc: Graph-based Visual Document ClassificationCode0
ARL2: Aligning Retrievers for Black-box Large Language Models via Self-guided Adaptive Relevance LabelingCode0
Learning unbiased zero-shot semantic segmentation networks via transductive transferCode0
Growing Neural Network with Shared ParameterCode0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
GTNet: Generative Transfer Network for Zero-Shot Object DetectionCode0
GYM at Qur’an QA 2023 Shared Task: Multi-Task Transfer Learning for Quranic Passage Retrieval and Question Answering with Large Language ModelsCode0
Group-level Emotion Recognition using Transfer Learning from Face IdentificationCode0
Advances in deep learning methods for pavement surface crack detection and identification with visible light visual imagesCode0
Hacking Task Confounder in Meta-LearningCode0
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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