https://github.com/dminus1/LLM-OOD-control
공식 코드Controlling Out-of-Domain Gaps in LLMs for Genre Classification and Generated Text Detection
Papers with Code 아카이브
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/dminus1/genre
공식 코드BERT Goes Off-Topic: Investigating the Domain Transfer Challenge using Genre Classification
https://github.com/dmlc/gluon-cv
공식 코드Bag of Tricks for Image Classification with Convolutional Neural Networks
https://github.com/dmskinson/dpmlbench
공식 코드DPMLBench: Holistic Evaluation of Differentially Private Machine Learning
https://github.com/dmtjepa/dmtjepa
공식 코드DMT-JEPA: Discriminative Masked Targets for Joint-Embedding Predictive Architecture
https://github.com/dniku/perceptual-gradient-networks
공식 코드https://github.com/dnn-security/Watermark-Robustness-Toolbox
공식 코드SoK: How Robust is Image Classification Deep Neural Network Watermarking? (Extended Version)
https://github.com/dobriban/data_aug
공식 코드https://github.com/doerlbh/BerlinUCB
공식 코드Online Semi-Supervised Learning in Contextual Bandits with Episodic Reward
https://github.com/doerlbh/UnsupervisedAttentionMechanism
공식 코드Unsupervised Attention Mechanism across Neural Network Layers
https://github.com/dongkyuk/CheXFusion
공식 코드https://github.com/donglao/depthtosemantic
공식 코드On the Viability of Monocular Depth Pre-training for Semantic Segmentation
https://github.com/dongliangchang/Mutual-Channel-Loss
공식 코드The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
https://github.com/dongliangchang/Top-Down-Spatial-Attention-Loss
공식 코드https://github.com/dongsky/lpt
공식 코드https://github.com/dongxzz/roli
공식 코드https://github.com/dongzelian/ssf
공식 코드Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning
https://github.com/dootmaan/icmil
공식 코드https://github.com/dosowiechi/mlmp
공식 코드Test-Time Adaptation of Vision-Language Models for Open-Vocabulary Semantic Segmentation
https://github.com/dotwang/dcn-t
공식 코드DCN-T: Dual Context Network with Transformer for Hyperspectral Image Classification
https://github.com/doviledo/transferability-in-medical-imaging
공식 코드https://github.com/dpclip/dpclip
공식 코드Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training
https://github.com/dpkingma/nips14-ssl
공식 코드https://github.com/dqmis/clip-mma
공식 코드https://github.com/dqshuai/metaformer
공식 코드MetaFormer: A Unified Meta Framework for Fine-Grained Recognition
https://github.com/dr-lingxiao/ag-man
공식 코드Attribute-Guided Multi-Level Attention Network for Fine-Grained Fashion Retrieval
https://github.com/drbeckmann/nr-cdt
공식 코드https://github.com/drcut/CPD
공식 코드https://github.com/drlxj/camdiff
공식 코드https://github.com/dropreg/R-Drop
공식 코드https://github.com/drsleep/nas-segm-pytorch
공식 코드Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
https://github.com/druidowm/OccamNet_Public
공식 코드OccamNet: A Fast Neural Model for Symbolic Regression at Scale
https://github.com/druidowm/occamnet_versions
공식 코드OccamNet: A Fast Neural Model for Symbolic Regression at Scale
https://github.com/ds7711/music_genre_classification
공식 코드Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification
https://github.com/dsam99/lol
공식 코드Losses over Labels: Weakly Supervised Learning via Direct Loss Construction
https://github.com/dsam99/pc_clip
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.