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 626–650 of 10307 papers

TitleStatusHype
Font Representation Learning via Paired-glyph MatchingCode1
Task Residual for Tuning Vision-Language ModelsCode1
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model EnsemblesCode1
Learning Causal Representations of Single Cells via Sparse Mechanism Shift ModelingCode1
Motion Style Transfer: Modular Low-Rank Adaptation for Deep Motion ForecastingCode1
Conditional Generative Models for Simulation of EMG During Naturalistic MovementsCode1
Low-Resource Music Genre Classification with Cross-Modal Neural Model ReprogrammingCode1
Phoneme Segmentation Using Self-Supervised Speech ModelsCode1
Amplifying Membership Exposure via Data PoisoningCode1
A simple, efficient and scalable contrastive masked autoencoder for learning visual representationsCode1
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification AlgorithmsCode1
Few-shot Image Generation via Adaptation-Aware Kernel ModulationCode1
Towards High-Quality Neural TTS for Low-Resource Languages by Learning Compact Speech RepresentationsCode1
Inducer-tuning: Connecting Prefix-tuning and Adapter-tuningCode1
Broken Neural Scaling LawsCode1
Will we run out of data? Limits of LLM scaling based on human-generated dataCode1
FedClassAvg: Local Representation Learning for Personalized Federated Learning on Heterogeneous Neural NetworksCode1
PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph CompletionCode1
A single-cell gene expression language modelCode1
Geometric Knowledge Distillation: Topology Compression for Graph Neural NetworksCode1
Neural Eigenfunctions Are Structured Representation LearnersCode1
Delving into Masked Autoencoders for Multi-Label Thorax Disease ClassificationCode1
Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural NetworksCode1
Surgical Fine-Tuning Improves Adaptation to Distribution ShiftsCode1
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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