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On Randomized Classification Layers and Their Implications in Natural Language Generation
In natural language generation tasks, a neural language model is used for generating a sequence of words forming a sentence.
A Baseline for Detecting Out-of-Distribution Examples in Image Captioning
Image captioning research achieved breakthroughs in recent years by developing neural models that can generate diverse and high-quality descriptions for images drawn from the same distribution as training images.
Redesigning the classification layer by randomizing the class representation vectors
We propose to draw the class vectors randomly and set them as fixed during training, thus invalidating the visual similarities encoded in these vectors.
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