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Found 12 papers, 9 papers with code
Adaptive Discovering and Merging for Incremental Novel Class Discovery
One important desideratum of lifelong learning aims to discover novel classes from unlabelled data in a continuous manner.
AutoAgents: A Framework for Automatic Agent Generation
Therefore, we introduce AutoAgents, an innovative framework that adaptively generates and coordinates multiple specialized agents to build an AI team according to different tasks.
LLaSM: Large Language and Speech Model
In this work, we propose Large Language and Speech Model (LLaSM).
Learning Sparse Neural Networks with Identity Layers
Applying such theory, we propose a plug-and-play CKA-based Sparsity Regularization for sparse network training, dubbed CKA-SR, which utilizes CKA to reduce feature similarity between layers and increase network sparsity.
Picking Up Quantization Steps for Compressed Image Classification
The sensitivity of deep neural networks to compressed images hinders their usage in many real applications, which means classification networks may fail just after taking a screenshot and saving it as a compressed file.
Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey
We also give visualization and analysis of the model parameters and results on representative downstream tasks.
Training Full Spike Neural Networks via Auxiliary Accumulation Pathway
The accumulation in AAP could compensate for the information loss during the forward and backward of full spike propagation, and facilitate the training of the FSNN.
OpenOOD: Benchmarking Generalized Out-of-Distribution Detection
Out-of-distribution (OOD) detection is vital to safety-critical machine learning applications and has thus been extensively studied, with a plethora of methods developed in the literature.
Amplitude-Phase Recombination: Rethinking Robustness of Convolutional Neural Networks in Frequency Domain
This observation leads to more explanations of the CNN's generalization behaviors in both robustness to common perturbations and out-of-distribution detection, and motivates a new perspective on data augmentation designed by re-combing the phase spectrum of the current image and the amplitude spectrum of the distracter image.
Ranked #7 on
Out-of-Distribution Detection
on CIFAR-10
Adversarial Reciprocal Points Learning for Open Set Recognition
Then, an adversarial margin constraint is proposed to reduce the open space risk by limiting the latent open space constructed by reciprocal points.
Annotation-Efficient Untrimmed Video Action Recognition
In this paper, we target a new problem, Annotation-Efficient Video Recognition, to reduce the requirement of annotations for both large amount of samples and the action location.
Learning Open Set Network with Discriminative Reciprocal Points
In this process, one of the key challenges is to reduce the risk of generalizing the inherent characteristics of numerous unknown samples learned from a small amount of known data.
