SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8726–8750 of 10420 papers

TitleStatusHype
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles—0
HIGHLY EFFICIENT 8-BIT LOW PRECISION INFERENCE OF CONVOLUTIONAL NEURAL NETWORKS—0
How Training Data Affect the Accuracy and Robustness of Neural Networks for Image Classification—0
Causal importance of orientation selectivity for generalization in image recognitionCode0
Model Compression with Generative Adversarial Networks—0
Asynchronous SGD without gradient delay for efficient distributed training—0
A Synaptic Neural Network and Synapse LearningCode0
Multi-way Encoding for Robustness to Adversarial Attacks—0
On Expected Accuracy—0
Optimal Attacks against Multiple Classifiers—0
Probabilistic Federated Neural Matching—0
Probabilistic Model-Based Dynamic Architecture Search—0
Radial Basis Feature Transformation to Arm CNNs Against Adversarial Attacks—0
Select Via Proxy: Efficient Data Selection For Training Deep Networks—0
signSGD via Zeroth-Order Oracle—0
Sufficient Conditions for Robustness to Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks—0
Test Selection for Deep Learning Systems—0
Deep Transfer Learning for Few-Shot SAR Image Classification—0
PR Product: A Substitute for Inner Product in Neural NetworksCode0
HOG feature extraction from encrypted images for privacy-preserving machine learning—0
Self-Attention Capsule Networks for Object Classification—0
Domain Agnostic Learning with Disentangled RepresentationsCode0
Collage Inference: Using Coded Redundancy for Low Variance Distributed Image Classification—0
Analysis of Confident-Classifiers for Out-of-distribution DetectionCode0
Forget the Learning Rate, Decay Loss—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5DaViT-HTop 1 Accuracy90.2—Unverified
6Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10Meta Pseudo Labels (EfficientNet-B6-Wide)Top 1 Accuracy90—Unverified