https://github.com/Coderx7/SimpleNet
공식 코드Lets keep it simple, Using simple architectures to outperform deeper and more complex architectures
Papers with Code 아카이브
Image Classification
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- 평가표
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- 보존된 평가 기록
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/CompactNet/CompactNet
공식 코드CompactNet: Platform-Aware Automatic Optimization for Convolutional Neural Networks
https://github.com/ComputationalRadiationPhysics/InFlow
공식 코드InFlow: Robust outlier detection utilizing Normalizing Flows
https://github.com/Computer-Vision-in-the-Wild/Elevater_Toolkit_IC
공식 코드ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models
https://github.com/ContrastToDivide/C2D
공식 코드Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
https://github.com/CuriousAI/mean-teacher
공식 코드https://github.com/DAMO-NLP-SG/DiGIT
공식 코드Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
https://github.com/DLR-RM/GRACE
공식 코드Density-based Feasibility Learning with Normalizing Flows for Introspective Robotic Assembly
https://github.com/DS3Lab/odlc
공식 코드https://github.com/DSE-MSU/R-transformer
공식 코드R-Transformer: Recurrent Neural Network Enhanced Transformer
https://github.com/DSPsleeporg/smiles-transformer
공식 코드SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery
https://github.com/DT42/squeezenet_demo
공식 코드SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/DTennant/GPC
공식 코드Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery
https://github.com/DTennant/distill_visual_priors
공식 코드https://github.com/DadianisBidza/LearnToPayAttention-Keras
공식 코드https://github.com/DannielSilva/RS-M-CLIP
공식 코드Multilingual Vision-Language Pre-training for the Remote Sensing Domain
https://github.com/Darel13712/pretrained-audio-representations
공식 코드Comparative Analysis of Pretrained Audio Representations in Music Recommender Systems
https://github.com/DataSciencePolimi/Visual-TCAV
공식 코드https://github.com/DavidMrd/Playing-to-distraction
공식 코드https://github.com/DearCaat/ioplin
공식 코드An Iteratively Optimized Patch Label Inference Network for Automatic Pavement Distress Detection
https://github.com/Debatrix/LifeLong-CLIP
공식 코드https://github.com/DebeshJha/LightLayers
공식 코드LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification
https://github.com/DecodEPFL/HamiltonianNet
공식 코드Hamiltonian Deep Neural Networks Guaranteeing Non-vanishing Gradients by Design
https://github.com/DeepLearningResearch/DEAL
공식 코드DEAL: Deep Evidential Active Learning for Image Classification
https://github.com/DeepScale/SqueezeNet
공식 코드SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
https://github.com/DenisTome/DeepPed
공식 코드https://github.com/DensoITLab/TeachAugment
공식 코드TeachAugment: Data Augmentation Optimization Using Teacher Knowledge
https://github.com/DequanWang/CoDeNet
공식 코드https://github.com/DequanWang/tent
공식 코드https://github.com/Derek-Wds/MAD-VAE
공식 코드https://github.com/DiKorsch/deep_fve
공식 코드End-to-end Learning of a Fisher Vector Encoding for Part Features in Fine-grained Recognition
https://github.com/Dibyabha/dl-co2-pp
공식 코드Improving Power Plant CO2 Emission Estimation with Deep Learning and Satellite/Simulated Data
https://github.com/DingJianhao/stable-snn
공식 코드https://github.com/DingXiaoH/DiverseBranchBlock
공식 코드Diverse Branch Block: Building a Convolution as an Inception-like Unit
https://github.com/DingXiaoH/RepLKNet-pytorch
공식 코드Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
https://github.com/DingXiaoH/RepMLP
공식 코드RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.