Image Classification
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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/alinlab/M2m
공식 코드M2m: Imbalanced Classification via Major-to-minor Translation
https://github.com/alinlab/MASKER
공식 코드MASKER: Masked Keyword Regularization for Reliable Text Classification
https://github.com/alinlab/b2t
공식 코드Discovering and Mitigating Visual Biases through Keyword Explanation
https://github.com/alinlab/cs-kd
공식 코드Regularizing Class-wise Predictions via Self-knowledge Distillation
https://github.com/alinlab/homer
공식 코드Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs
https://github.com/alinlab/psco
공식 코드Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
https://github.com/alirezaafzalaghaei/fKAN
공식 코드fKAN: Fractional Kolmogorov-Arnold Networks with trainable Jacobi basis functions
https://github.com/alirezaafzalaghaei/rkan
공식 코드https://github.com/alirezaazadbakht/kernel-sharing
공식 코드Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
https://github.com/alitourani/Iranis-dataset
공식 코드Iranis: A Large-scale Dataset of Farsi License Plate Characters
https://github.com/aliutkus/spe
공식 코드Relative Positional Encoding for Transformers with Linear Complexity
https://github.com/aliyun/alibabacloud-quantization-networks
공식 코드https://github.com/allanyangzhou/generative-invariance-transfer
공식 코드https://github.com/allegro/allenoise
공식 코드AlleNoise: large-scale text classification benchmark dataset with real-world label noise
https://github.com/allen-piexl/deepsensor
공식 코드Excitement Surfeited Turns to Errors: Deep Learning Testing Framework Based on Excitable Neurons
https://github.com/allenai/container
공식 코드https://github.com/allenai/hidden-networks
공식 코드https://github.com/allenai/reclip
공식 코드ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension
https://github.com/allenbai01/cp-gen
공식 코드Efficient and Differentiable Conformal Prediction with General Function Classes
https://github.com/alpha-vl/convmae
공식 코드https://github.com/alphawuseu/mlfm
공식 코드https://github.com/alphaxia/papi
공식 코드Towards Effective Visual Representations for Partial-Label Learning
https://github.com/alsace08/ood-math-reasoning
공식 코드Embedding Trajectory for Out-of-Distribution Detection in Mathematical Reasoning
https://github.com/alshedivat/cen
공식 코드https://github.com/altair199797/lowformer
공식 코드LowFormer: Hardware Efficient Design for Convolutional Transformer Backbones
https://github.com/altndrr/lmms-owc
공식 코드https://github.com/altndrr/vic
공식 코드https://github.com/altndrr/vicss
공식 코드Vocabulary-free Image Classification and Semantic Segmentation
https://github.com/amaiya/ktrain
공식 코드https://github.com/amayags/mustang
공식 코드https://github.com/amazon-research/exponential-moving-average-normalization
공식 코드Exponential Moving Average Normalization for Self-supervised and Semi-supervised Learning
https://github.com/amazon-research/long-tailed-ood-detection
공식 코드https://github.com/amazon-research/network-deconvolution-pp
공식 코드https://github.com/amazon-science/few-shot-baseline
공식 코드https://github.com/amazon-science/prompt-pretraining
공식 코드Prompt Pre-Training with Twenty-Thousand Classes for Open-Vocabulary Visual Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.