https://github.com/pulp-platform/fscil
공식 코드12 mJ per Class On-Device Online Few-Shot Class-Incremental Learning
Papers with Code 아카이브
Image Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 33
- 평가표
- 177
- 보존된 평가 기록
- 2,906
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/pulp-platform/pulp-trainlib
공식 코드https://github.com/pulp-platform/stream-ebpc
공식 코드EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators
https://github.com/pupiu45/FedGO
공식 코드Provably Near-Optimal Federated Ensemble Distillation with Negligible Overhead
https://github.com/pure-melo/s2mamba
공식 코드S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification
https://github.com/putshua/inference-scale-ann-snn
공식 코드Inference-Scale Complexity in ANN-SNN Conversion for High-Performance and Low-Power Applications
https://github.com/pvbhanuteja/mixrnet
공식 코드Using mixup as regularization and tuning hyper-parameters for ResNets
https://github.com/pxiangwu/PLC
공식 코드Learning with Feature-Dependent Label Noise: A Progressive Approach
https://github.com/pyjulie/medical-vlms-ood-detection
공식 코드Delving into Out-of-Distribution Detection with Medical Vision-Language Models
https://github.com/pyjulie/monica
공식 코드MONICA: Benchmarking on Long-tailed Medical Image Classification
https://github.com/pyjulie/ussl4mic
공식 코드Universal Semi-Supervised Learning for Medical Image Classification
https://github.com/pyt19/protosolo
공식 코드One Prototype Is Enough: Single-Prototype Activation for Interpretable Image Classification
https://github.com/pytorch/fairseq
공식 코드https://github.com/pytorch/fairseq/tree/main/examples/data2vec
공식 코드data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language
https://github.com/pytorch/vision
공식 코드TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation
https://github.com/qampspring2023/qgpt-issue-31
공식 코드https://github.com/qcao10/DamageDetection
공식 코드https://github.com/qhfan/CloFormer
공식 코드Rethinking Local Perception in Lightweight Vision Transformer
https://github.com/qiangyao1988/ia-vit
공식 코드https://github.com/qigongsun/BMD
공식 코드Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration
https://github.com/qilong-zhang/Patch-wise-iterative-attack
공식 코드https://github.com/qimaqi/Implicit-Zoo
공식 코드Implicit-Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D Scenes
https://github.com/qin87/scalenet
공식 코드https://github.com/qinenergy/adanet
공식 코드Semi-Supervised Learning by Augmented Distribution Alignment
https://github.com/qingjie99/MetFA
공식 코드Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment
https://github.com/qingruzhang/platon
공식 코드PLATON: Pruning Large Transformer Models with Upper Confidence Bound of Weight Importance
https://github.com/qingyangzhang/dul
공식 코드The Best of Both Worlds: On the Dilemma of Out-of-distribution Detection
https://github.com/qinyu-allen-zhao/lvlm-lp
공식 코드The First to Know: How Token Distributions Reveal Hidden Knowledge in Large Vision-Language Models?
https://github.com/qinyu-allen-zhao/optfsood
공식 코드Towards Optimal Feature-Shaping Methods for Out-of-Distribution Detection
https://github.com/qiqi-helloworld/absgd
공식 코드https://github.com/qitianwu/graphood-gnnsafe
공식 코드Energy-based Out-of-Distribution Detection for Graph Neural Networks
https://github.com/qitianwu/nodeformer
공식 코드NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
https://github.com/qiujiaming315/edgeml-dqn
공식 코드Adaptive Edge Offloading for Image Classification Under Rate Limit
https://github.com/qiujumin/don-rl
공식 코드Decision-making and control with diffractive optical networks
https://github.com/qiukunpeng/dscl
공식 코드Learn From Zoom: Decoupled Supervised Contrastive Learning For WCE Image Classification
https://github.com/qiulingxu/FeatureSpaceAttack
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.