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 7951–8000 of 10420 papers

TitleStatusHype
Constrained Linear Data-feature Mapping for Image ClassificationCode0
Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion ReductionCode0
Federated Learning with Bayesian Differential Privacy—0
Optimizing Data Usage via Differentiable RewardsCode0
Attack Agnostic Statistical Method for Adversarial Detection—0
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation AnchoringCode1
Classification-driven Single Image Dehazing—0
EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble TransformationsCode0
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural ArchitectureCode0
Rethinking Normalization and Elimination Singularity in Neural NetworksCode0
Beyond Synthetic Noise: Deep Learning on Controlled Noisy LabelsCode1
Filter Response Normalization Layer: Eliminating Batch Dependence in the Training of Deep Neural NetworksCode0
Regularizing Neural Networks by Stochastically Training Layer EnsemblesCode0
Quantization NetworksCode0
Adversarial Examples Improve Image RecognitionCode0
MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks—0
AdaFilter: Adaptive Filter Fine-tuning for Deep Transfer Learning—0
Outside the Box: Abstraction-Based Monitoring of Neural NetworksCode0
MetH: A family of high-resolution and variable-shape image challengesCode0
Deep Learning based HEp-2 Image Classification: A Comprehensive Review—0
Inspect Transfer Learning Architecture with Dilated Convolution—0
Auto-Precision Scaling for Distributed Deep LearningCode0
Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation ApproachCode0
Visualization approach to assess the robustness of neural networks for medical image classification—0
IC-Network: Efficient Structure for Convolutional Neural Networks—0
IFQ-Net: Integrated Fixed-point Quantization Networks for Embedded Vision—0
Rethinking deep active learning: Using unlabeled data at model trainingCode0
General E(2)-Equivariant Steerable CNNsCode1
Crowd Counting via Segmentation Guided Attention Networks and Curriculum LossCode0
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)—0
Learning Permutation Invariant Representations using Memory NetworksCode0
Feedback Control for Online Training of Neural Networks—0
Commit2Vec: Learning Distributed Representations of Code Changes—0
Constructing Multiple Tasks for Augmentation: Improving Neural Image Classification With K-means FeaturesCode0
Fine-Grained Neural Architecture Search—0
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual RecognitionCode0
Learning with Hierarchical Complement Objective—0
Coverage Testing of Deep Learning Models using Dataset Characterization—0
Selective sampling for accelerating training of deep neural networksCode0
Automatic Design of CNNs via Differentiable Neural Architecture Search for PolSAR Image Classification—0
Explanatory Masks for Neural Network Interpretability—0
DeepSat V2: Feature Augmented Convolutional Neural Nets for Satellite Image ClassificationCode0
Label-similarity Curriculum Learning—0
TinyCNN: A Tiny Modular CNN Accelerator for Embedded FPGA—0
In-domain representation learning for remote sensingCode0
Simple iterative method for generating targeted universal adversarial perturbationsCode0
Adversarial Embedding: A robust and elusive Steganography and Watermarking technique—0
Self-Supervised Learning For Few-Shot Image ClassificationCode0
Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer—0
Adversarial Transformations for Semi-Supervised Learning—0
Show:102550
← PrevPage 160 of 209Next →
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
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 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
10RevCol-HTop 1 Accuracy90—Unverified