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Found 7 papers, 2 papers with code
On the Stability-Plasticity Dilemma of Class-Incremental Learning
A primary goal of class-incremental learning is to strike a balance between stability and plasticity, where models should be both stable enough to retain knowledge learned from previously seen classes, and plastic enough to learn concepts from new classes.
Learning Semantic Segmentation from Multiple Datasets with Label Shifts
First, a gradient conflict in training due to mismatched label spaces is identified and a class-independent binary cross-entropy loss is proposed to alleviate such label conflicts.
MILP-based optimal day-ahead scheduling for system-centric CEMS supporting different types of homes and energy trading
Moreover, by using linear transformation, the computational time of the optimization problem of the proposed system-centric CEMS is only 118. 2 s for a 500-home community, which is a short time for day-ahead scheduling of a community.
Learning Debiased and Disentangled Representations for Semantic Segmentation
By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes, and the model is able to learn more debiased and disentangled feature representations.
CoSMo: Content-Style Modulation for Image Retrieval With Text Feedback
We focus on designing an image-text compositor, i. e., integrating multi-modal inputs to produce a representation similar to that of the target image.
Ranked #15 on
Image Retrieval
on Fashion IQ
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
Recent works on domain adaptation exploit adversarial training to obtain domain-invariant feature representations from the joint learning of feature extractor and domain discriminator networks.
Ranked #16 on
Domain Adaptation
on VisDA2017
Learning to Optimize Domain Specific Normalization for Domain Generalization
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains.
Ranked #3 on
Unsupervised Domain Adaptation
on PACS
