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Found 8 papers, 3 papers with code
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images
Knowledge distillation (KD) has been a popular and effective method for model compression.
Inducing Neural Collapse to a Fixed Hierarchy-Aware Frame for Reducing Mistake Severity
There is a recently discovered and intriguing phenomenon called Neural Collapse: at the terminal phase of training a deep neural network for classification, the within-class penultimate feature means and the associated classifier vectors of all flat classes collapse to the vertices of a simplex Equiangular Tight Frame (ETF).
Enhancing Self-Training Methods
Semi-supervised learning approaches train on small sets of labeled data along with large sets of unlabeled data.
Learning When to Say "I Don't Know"
We propose a new Reject Option Classification technique to identify and remove regions of uncertainty in the decision space for a given neural classifier and dataset.
Confident AI
In this paper, we propose "Confident AI" as a means to designing Artificial Intelligence (AI) and Machine Learning (ML) systems with both algorithm and user confidence in model predictions and reported results.
Revisiting Batch Norm Initialization
Standard initialization of each BN in a network sets the affine transformation scale and shift to 1 and 0, respectively.
Bottom-up Hierarchical Classification Using Confusion-based Logit Compression
In this work, we propose a method to efficiently compute label posteriors of a base flat classifier in the presence of few validation examples within a bottom-up hierarchical inference framework.
Posterior Adaptation With New Priors
Classification approaches based on the direct estimation and analysis of posterior probabilities will degrade if the original class priors begin to change.
