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 9026–9050 of 10420 papers

TitleStatusHype
Thwarting Adversarial Examples: An L_0-RobustSparse Fourier Transform—0
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features—0
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network—0
Reproduction Report on "Learn to Pay Attention"Code0
Layer-Parallel Training of Deep Residual Neural NetworksCode0
Defending Against Universal Perturbations With Shared Adversarial Training—0
Feature Denoising for Improving Adversarial RobustnessCode0
Detecting Adversarial Examples in Convolutional Neural Networks—0
Variational Saccading: Efficient Inference for Large Resolution ImagesCode0
Adversarial Defense of Image Classification Using a Variational Auto-EncoderCode0
Optimizing speed/accuracy trade-off for person re-identification via knowledge distillation—0
Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic ForgettingCode0
Fooling Network Interpretation in Image Classification—0
Teacher-Student Compression with Generative Adversarial NetworksCode0
Blockchain Enabled Trustless API Marketplace—0
Ladder Networks for Semi-Supervised Hyperspectral Image Classification—0
Auto-tuning TensorFlow Threading Model for CPU Backend—0
Energy Efficient Hardware for On-Device CNN Inference via Transfer Learning—0
Prototype-based Neural Network Layers: Incorporating Vector Quantization—0
Parameter Re-Initialization through Cyclical Batch Size Schedules—0
Singing Voice Separation Using a Deep Convolutional Neural Network Trained by Ideal Binary Mask and Cross EntropyCode0
Universal Perturbation Attack Against Image Retrieval—0
Adversarial Domain Randomization—0
Knowledge Distillation with Feature Maps for Image Classification—0
Deep Learning for Classical Japanese LiteratureCode0
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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