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Found 10 papers, 3 papers with code
Cross-Domain Feature Augmentation for Domain Generalization
Existing methods focus on learning invariance across domains to enhance model robustness, and data augmentation has been widely used to learn invariant predictors, with most methods performing augmentation in the input space.
Towards Robust Out-of-Distribution Generalization Bounds via Sharpness
To bridge this gap between optimization and OOD generalization, we study the effect of sharpness on how a model tolerates data change in domain shift which is usually captured by "robustness" in generalization.
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.
Unraveling Model-Agnostic Meta-Learning via The Adaptation Learning Rate
In this paper, we study the effect of the adaptation learning rate in meta-learning with mixed linear regression.
Image-to-Video Generation via 3D Facial Dynamics
Video generation from a single face image is an interesting problem and usually tackled by utilizing Generative Adversarial Networks (GANs) to integrate information from the input face image and a sequence of sparse facial landmarks.
PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment
In this paper, we tackle the challenging few-shot segmentation problem from a metric learning perspective and present PANet, a novel prototype alignment network to better utilize the information of the support set.
Panoptic Edge Detection
In this work, we address a new finer-grained task, termed panoptic edge detection (PED), which aims at predicting semantic-level boundaries for stuff categories and instance-level boundaries for instance categories, in order to provide more comprehensive and unified scene understanding from the perspective of edges. We then propose a versatile framework, Panoptic Edge Network (PEN), which aggregates different tasks of object detection, semantic and instance edge detection into a single holistic network with multiple branches.
Hierarchical Meta Learning
Extensive experiments on few-shot classification and regression problems clearly demonstrate the superiority of HML over fine-tuning and state-of-the-art meta learning approaches in terms of generalization across heterogeneous tasks.
Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks
In this paper, we propose a novel GAN based on inter-frame difference to circumvent the difficulties.
Spatial-Temporal Synergic Residual Learning for Video Person Re-Identification
We present a novel method - Spatial-Temporal Synergic Residual Network (STSRN) for this problem.
