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Although the applications of artificial intelligence especially deep learning had greatly improved various aspects of intelligent manufacturing, they still face challenges for wide employment due to the poor generalization ability, difficulties to establish high-quality training datasets, and unsatisfactory performance of deep learning methods.
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Scotch and soda: A transformer video shadow detection framework
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Cheap lunch for medical image segmentation by fine-tuning sam on few exemplars, 2023
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Fd-align: Feature discrimination alignment for fine-tuning pre-trained models in few-shot learning, 2023
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A new generation? a discussion on deep generative models in supply chains
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Unsupervised human activity recognition through two-stage prompting with chatgpt, 2023
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Expel: Llm agents are experiential learners
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Multimodal transformer for bearing fault diagnosis: A new method based on frequency-time feature decomposition
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Effective data augmentation with diffusion models, 2023
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Llm-assisted code cleaning for training accurate code generators, 2023
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Desam: Decoupling segment anything model for generalizable medical image segmentation, 2023
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How segment anything model (sam) boost medical image segmentation?
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Samaug: Point prompt augmentation for segment anything model
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On the robustness of segment anything
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Can sam segment anything? when sam meets camouflaged object detection
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Camouflaged object detection with feature decomposition and edge reconstruction
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Skinsam: Empowering skin cancer segmentation with segment anything model
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Fine-tune language models to approximate unbiased in-context learning, 2023
Timothy Chu, Zhao Song, and Chiwun Yang · 2023
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Qlora: Efficient finetuning of quantized llms
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Fate-llm: A industrial grade federated learning framework for large language models, 2023
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Faithful explanations of black-box nlp models using llm-generated counterfactuals, 2023
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Are both generative ai and chatgpt game changers for 21st-century operations and supply chain excellence?
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Revolutionizing supply chain management with ai and chatgpt
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Spear or shield: Leveraging generative ai to tackle security threats of intelligent network services, 2023
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Identifying and mitigating the security risks of generative ai, 2023
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Impress: Evaluating the resilience of imperceptible perturbations against unauthorized data usage in diffusion-based generative ai, 2023
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Llm self defense: By self examination, llms know they are being tricked, 2023
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Defending against alignment-breaking attacks via robustly aligned llm, 2023
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A simple recipe for competitive low-compute self supervised vision models
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