https://github.com/mcmahon-lab/image-sensing-with-multilayer-nonlinear-optical-neural-networks
공식 코드Image sensing with multilayer, nonlinear optical neural networks
Papers with Code 아카이브
Image Classification
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- 평가표
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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/mcrl/CyCNN
공식 코드CyCNN: A Rotation Invariant CNN using Polar Mapping and Cylindrical Convolution Layers
https://github.com/mdabbah/COOD_benchmarking
공식 코드A framework for benchmarking class-out-of-distribution detection and its application to ImageNet
https://github.com/mdca-loss/mdca-calibration
공식 코드A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration
https://github.com/mdppml/tea
공식 코드Accelerating Targeted Hard-Label Adversarial Attacks in Low-Query Black-Box Settings
https://github.com/meclabtuda/atlas-replay
공식 코드https://github.com/med-air/dltta
공식 코드DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images
https://github.com/med-air/harmofl
공식 코드HarmoFL: Harmonizing Local and Global Drifts in Federated Learning on Heterogeneous Medical Images
https://github.com/med-air/ttadc
공식 코드https://github.com/megamine25/Music-genre-classification
공식 코드https://github.com/megvii-model/DetNAS
공식 코드https://github.com/megvii-research/Arch-Net
공식 코드Arch-Net: Model Distillation for Architecture Agnostic Model Deployment
https://github.com/megvii-research/FSSD_OoD_Detection
공식 코드https://github.com/megvii-research/ICD
공식 코드Instance-Conditional Knowledge Distillation for Object Detection
https://github.com/megvii-research/caspl
공식 코드Cascade Prompt Learning for Vision-Language Model Adaptation
https://github.com/megvii-research/mdistiller
공식 코드https://github.com/megvii-research/replknet
공식 코드Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs
https://github.com/megvii-research/revcol
공식 코드https://github.com/megvii-research/tps-cvpr2023
공식 코드Joint Token Pruning and Squeezing Towards More Aggressive Compression of Vision Transformers
https://github.com/mehravehj/Balanced-Mixture-of-SuperNets
공식 코드Balanced Mixture of SuperNets for Learning the CNN Pooling Architecture
https://github.com/mehrdad-noori/watt
공식 코드https://github.com/meituan-automl/mobilevlm
공식 코드MobileVLM : A Fast, Strong and Open Vision Language Assistant for Mobile Devices
https://github.com/meituan-automl/visionllama
공식 코드https://github.com/melanibe/distance_matters_performance_estimation
공식 코드Distance Matters For Improving Performance Estimation Under Covariate Shift
https://github.com/melanibe/failure_detection_benchmark
공식 코드Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed
https://github.com/membrai/deblurringmim
공식 코드Deblurring Masked Autoencoder is Better Recipe for Ultrasound Image Recognition
https://github.com/mendelxu/zsseg.baseline
공식 코드A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model
https://github.com/mengliupurdue/graph-topological-data-analysis
공식 코드https://github.com/mengxianghan123/PRO-DSC
공식 코드Exploring a Principled Framework for Deep Subspace Clustering
https://github.com/mengyuanchen21/neurips2024-csp
공식 코드Conjugated Semantic Pool Improves OOD Detection with Pre-trained Vision-Language Models
https://github.com/merlresearch/partial_gcnn
공식 코드https://github.com/mertduman/zero-shot-ecg
공식 코드https://github.com/mesaysama/amharic-handwritten-character-dataset
공식 코드Offline Handwritten Amharic Character Recognition Using Few-shot Learning
https://github.com/meshiba/sppi
공식 코드https://github.com/metehancekic/sparsestrongactivations
공식 코드Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations
https://github.com/meteor-stars/gcdm_adaptivenet
공식 코드Enhancing Adaptive Deep Networks for Image Classification via Uncertainty-aware Decision Fusion
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.