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Found 10 papers, 5 papers with code
VisIT-Bench: A Benchmark for Vision-Language Instruction Following Inspired by Real-World Use
These descriptions enable 1) collecting human-verified reference outputs for each instance; and 2) automatic evaluation of candidate multimodal generations using a text-only LLM, aligning with human judgment.
Data Feedback Loops: Model-driven Amplification of Dataset Biases
Datasets scraped from the internet have been critical to the successes of large-scale machine learning.
Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning
The development of CLIP [Radford et al., 2021] has sparked a debate on whether language supervision can result in vision models with more transferable representations than traditional image-only methods.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments.
Transposer: Universal Texture Synthesis Using Feature Maps as Transposed Convolution Filter
Specifically, we directly treat the whole encoded feature map of the input texture as transposed convolution filters and the features' self-similarity map, which captures the auto-correlation information, as input to the transposed convolution.
Measuring Robustness to Natural Distribution Shifts in Image Classification
We study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets.
Ranked #47 on
Domain Generalization
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Autoregressive Models: What Are They Good For?
Autoregressive (AR) models have become a popular tool for unsupervised learning, achieving state-of-the-art log likelihood estimates.
When Robustness Doesn’t Promote Robustness: Synthetic vs. Natural Distribution Shifts on ImageNet
We conduct a large experimental comparison of various robustness metrics for image classification.
Targeted Adversarial Examples for Black Box Audio Systems
The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
