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Found 8 papers, 4 papers with code
ControlLLM: Augment Language Models with Tools by Searching on Graphs
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks.
ADDP: Learning General Representations for Image Recognition and Generation with Alternating Denoising Diffusion Process
In each denoising step, our method first decodes pixels from previous VQ tokens, then generates new VQ tokens from the decoded pixels.
Denoising Diffusion Semantic Segmentation with Mask Prior Modeling
In this paper, we propose to ameliorate the semantic segmentation quality of existing discriminative approaches with a mask prior modeled by a recently-developed denoising diffusion generative model.
Dual-Alignment Pre-training for Cross-lingual Sentence Embedding
Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding.
Detachedly Learn a Classifier for Class-Incremental Learning
In continual learning, model needs to continually learn a feature extractor and classifier on a sequence of tasks.
Bypassing Logits Bias in Online Class-Incremental Learning with a Generative Framework
In our framework, a generative classifier which utilizes replay memory is used for inference, and the training objective is a pair-based metric learning loss which is proven theoretically to optimize the feature space in a generative way.
Alleviating Representational Shift for Continual Fine-tuning
We study a practical setting of continual learning: fine-tuning on a pre-trained model continually.
A Sinogram Inpainting Method based on Generative Adversarial Network for Limited-angle Computed Tomography
Limited-angle computed tomography (CT) image reconstruction is a challenging reconstruction problem in the fields of CT. With the development of deep learning, the generative adversarial network (GAN) perform well in image restoration by approximating the distribution of training sample data.
Medical Physics
