PAPER REVIEW

• [X] Generative Adversarial Networks
• [X] Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
• [X] Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• [X] Conditional Generative Adversarial Nets
• [X] pix2pix
• [X] StarGANs
• [ ] Contrastive Learning for Unpaired Image-to-Image Translation
• [X] ViT
• [X] MLP-Mixer
• [X] Towards General Purpose Vision Systems (GPV)
• [X] SelfPatch
• [ ] Towards Total Recall in Industrial Anomaly Detection (PatchCore)
• [X] Emerging Properties of Self-Supervised Vision Transformers (DINO)
• [X] SimCLR
• [ ] MoCo
• [ ] MoCoV3
• [ ] BYOL
• [ ] SwAV
• [X] What do Self-Supervised ViT learn?
• [X] MAE
• [X] iBOT
• [X] UniAD
• [X] MetaFormer
• [X] Unified-IO
• [X] BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation
• [X] LARGE LANGUAGE MODELS ARE HUMAN-LEVEL PROMPT ENGINEERS
• [ ] Learning Transferable Visual Models From Natural Language Supervision
• [X] WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
• [X] Instruction Induction: From Few Examples to Natural Language Task Descriptions
• [ ] OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework
• [ ] Optimizing Prompts for Text-to-Image Generation
• [ ] Grounded Language-Image Pre-training
• [X] Universal Few-shot Learning Of Dense Prediction Tasks With Viual Token Matching
• [ ] Anomaly Detection Requires Better Representations
• [ ] Towards Open World Object Detection
• [ ] Flamingo: a Visual Language Model for Few-Shot Learning
• [X] Open-vocabulary Object Detection via Vision and Language Knowledge Distillation
• [X] Learning to Prompt for Vision-Language Models
• [X] Conditional Prompt Learning For Vision-Language Models
• [ ] POUF: Prompt-oriented Unsupervised Fine-tuning For Large Pre-trained Models
• [X] SimpleNet: A Simple Network for Image Anomaly Detection and Localization