https://github.com/anishacharya/Optimization-Mavericks
공식 코드Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
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/anissa218/medfair_ukbb
공식 코드On Biases in a UK Biobank-based Retinal Image Classification Model
https://github.com/ankurderia/mft
공식 코드Multimodal Fusion Transformer for Remote Sensing Image Classification
https://github.com/ankurmallick/dpkl
공식 코드Deep Kernels with Probabilistic Embeddings for Small-Data Learning
https://github.com/anniesch/jtt
공식 코드Just Train Twice: Improving Group Robustness without Training Group Information
https://github.com/anok3111/bc-sam
공식 코드https://github.com/anokland/local-loss
공식 코드https://github.com/anonymous-2491/drop-clause-interpretable-tm
공식 코드Drop Clause: Enhancing Performance, Interpretability and Robustness of the Tsetlin Machine
https://github.com/anonymous-p/Flickering_Adversarial_Video
공식 코드Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks
https://github.com/anonymous0120/dr
공식 코드Enhancing Cross-task Black-Box Transferability of Adversarial Examples with Dispersion Reduction
https://github.com/ans92/tlac
공식 코드TLAC: Two-stage LMM Augmented CLIP for Zero-Shot Classification
https://github.com/ansh941/MnistSimpleCNN
공식 코드An Ensemble of Simple Convolutional Neural Network Models for MNIST Digit Recognition
https://github.com/anshulbshah/MMCL
공식 코드https://github.com/ant-uni-bremen/sinfony
공식 코드https://github.com/anthropics/hh-rlhf
공식 코드Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
https://github.com/antidotert/antidotert
공식 코드AntidoteRT: Run-time Detection and Correction of Poison Attacks on Neural Networks
https://github.com/antonbaumann/mimo-unet
공식 코드Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression
https://github.com/antonxue/chordal-lipsdp
공식 코드Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
https://github.com/antoyang/NAS-Benchmark
공식 코드https://github.com/anugyas/ood-on-nae
공식 코드https://github.com/anujinho/trident
공식 코드Transductive Decoupled Variational Inference for Few-Shot Classification
https://github.com/anuraganands/Non-image-data-classification-with-CNN
공식 코드Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis
https://github.com/anvoynov/BigGANsAreWatching
공식 코드Object Segmentation Without Labels with Large-Scale Generative Models
https://github.com/aoluming/IDKG
공식 코드https://github.com/aomandechenai/deep-probability-aggregation-clustering
공식 코드https://github.com/aounon/center-smoothing
공식 코드Center Smoothing: Certified Robustness for Networks with Structured Outputs
https://github.com/apardyl/protomil
공식 코드ProtoMIL: Multiple Instance Learning with Prototypical Parts for Whole-Slide Image Classification
https://github.com/aplesner/flip-reasoning-challenge
공식 코드https://github.com/apple/corenet
공식 코드Bytes Are All You Need: Transformers Operating Directly On File Bytes
https://github.com/apple/ml-ademamix
공식 코드https://github.com/apple/ml-aim
공식 코드https://github.com/apple/ml-capsules-inverted-attention-routing
공식 코드https://github.com/apple/ml-cvnets
공식 코드MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer
https://github.com/apple/ml-fastvit
공식 코드FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization
https://github.com/apple/ml-mixupmp
공식 코드Posterior Uncertainty Quantification in Neural Networks using Data Augmentation
https://github.com/apple/ml-mobileone
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.