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Found 8 papers, 3 papers with code
Heterogeneous Multi-task Learning with Expert Diversity
In contrast to single-task learning, in which a separate model is trained for each target, multi-task learning (MTL) optimizes a single model to predict multiple related targets simultaneously.
Chained Multi-stream Networks Exploiting Pose, Motion, and Appearance for Action Classification and Detection
In this paper, we propose a network architecture that computes and integrates the most important visual cues for action recognition: pose, motion, and the raw images.
Topometric Localization with Deep Learning
Compared to LiDAR-based localization methods, which provide high accuracy but rely on expensive sensors, visual localization approaches only require a camera and thus are more cost-effective while their accuracy and reliability typically is inferior to LiDAR-based methods.
Deep Semantic Classification for 3D LiDAR Data
To learn the distinction between movable and non-movable points in the environment, we introduce an approach based on deep neural network and for detecting the dynamic points, we estimate pointwise motion.
Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation
In order to further improve the architecture we introduce a weight function which aims to re-balance classes to increase the attention of the networks to under-represented objects.
DYNASHARE: DYNAMIC NEURAL NETWORKS FOR MULTI-TASK LEARNING
Parameter sharing approaches for deep multi-task learning share a common intuition: for a single network to perform multiple prediction tasks, the network needs to support multiple specialized execution paths.
Meta Temporal Point Processes
In this work, we propose to train TPPs in a meta learning framework, where each sequence is treated as a different task, via a novel framing of TPPs as neural processes (NPs).
AdaFlood: Adaptive Flood Regularization
Although neural networks are conventionally optimized towards zero training loss, it has been recently learned that targeting a non-zero training loss threshold, referred to as a flood level, often enables better test time generalization.
