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Found 6 papers, 3 papers with code
What Do Compressed Deep Neural Networks Forget?
However, this measure of performance conceals significant differences in how different classes and images are impacted by model compression techniques.
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning
Neural network pruning techniques have demonstrated it is possible to remove the majority of weights in a network with surprisingly little degradation to top-1 test set accuracy.
iCassava 2019 Fine-Grained Visual Categorization Challenge
Viral diseases are major sources of poor yields for cassava, the 2nd largest provider of carbohydrates in Africa. At least 80% of small-holder farmer households in Sub-Saharan Africa grow cassava.
Attention for Fine-Grained Categorization
This paper presents experiments extending the work of Ba et al. (2014) on recurrent neural models for attention into less constrained visual environments, specifically fine-grained categorization on the Stanford Dogs data set.
Zero-Shot Learning by Convex Combination of Semantic Embeddings
In other cases the semantic embedding space is established by an independent natural language processing task, and then the image transformation into that space is learned in a second stage.
Ranked #8 on
Multi-label zero-shot learning
on Open Images V4
DeViSE: A Deep Visual-Semantic Embedding Model
Modern visual recognition systems are often limited in their ability to scale to large numbers of object categories.
Ranked #13 on
Zero-Shot Action Recognition
on Kinetics
