82 research outputs found
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement
Reliable application of machine learning is of primary importance to the
practical deployment of deep learning methods. A fundamental challenge is that
models are often unreliable due to overconfidence. In this paper, we estimate a
model's reliability by measuring \emph{the agreement between its latent space,
and the latent space of a foundation model}. However, it is challenging to
measure the agreement between two different latent spaces due to their
incoherence, \eg, arbitrary rotations and different dimensionality. To overcome
this incoherence issue, we design a \emph{neighborhood agreement measure}
between latent spaces and find that this agreement is surprisingly
well-correlated with the reliability of a model's predictions. Further, we show
that fusing neighborhood agreement into a model's predictive confidence in a
post-hoc way significantly improves its reliability. Theoretical analysis and
extensive experiments on failure detection across various datasets verify the
effectiveness of our method on both in-distribution and out-of-distribution
settings.Comment: ICML 202
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
"Effective robustness" measures the extra out-of-distribution (OOD)
robustness beyond what can be predicted from the in-distribution (ID)
performance. Existing effective robustness evaluations typically use a single
test set such as ImageNet to evaluate the ID accuracy. This becomes problematic
when evaluating models trained on different data distributions, e.g., comparing
models trained on ImageNet vs. zero-shot language-image pre-trained models
trained on LAION. In this paper, we propose a new evaluation metric to evaluate
and compare the effective robustness of models trained on different data. To do
this, we control for the accuracy on multiple ID test sets that cover the
training distributions for all the evaluated models. Our new evaluation metric
provides a better estimate of effective robustness when there are models with
different training data. It may also explain the surprising effective
robustness gains of zero-shot CLIP-like models exhibited in prior works that
used ImageNet as the only ID test set, while the gains diminish under our new
evaluation. Additional artifacts including interactive visualizations are
provided at https://shizhouxing.github.io/effective-robustness.Comment: NeurIPS 202
Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models
Studies on benign overfitting provide insights for the success of
overparameterized deep learning models. In this work, we examine whether
overfitting is truly benign in real-world classification tasks. We start with
the observation that a ResNet model overfits benignly on Cifar10 but not
benignly on ImageNet. To understand why benign overfitting fails in the
ImageNet experiment, we theoretically analyze benign overfitting under a more
restrictive setup where the number of parameters is not significantly larger
than the number of data points. Under this mild overparameterization setup, our
analysis identifies a phase change: unlike in the previous heavy
overparameterization settings, benign overfitting can now fail in the presence
of label noise. Our analysis explains our empirical observations, and is
validated by a set of control experiments with ResNets. Our work highlights the
importance of understanding implicit bias in underfitting regimes as a future
direction.Comment: Published as a conference paper at ICLR 202
Self-supervised video pretraining yields human-aligned visual representations
Humans learn powerful representations of objects and scenes by observing how
they evolve over time. Yet, outside of specific tasks that require explicit
temporal understanding, static image pretraining remains the dominant paradigm
for learning visual foundation models. We question this mismatch, and ask
whether video pretraining can yield visual representations that bear the
hallmarks of human perception: generalisation across tasks, robustness to
perturbations, and consistency with human judgements. To that end we propose a
novel procedure for curating videos, and develop a contrastive framework which
learns from the complex transformations therein. This simple paradigm for
distilling knowledge from videos, called VITO, yields general representations
that far outperform prior video pretraining methods on image understanding
tasks, and image pretraining methods on video understanding tasks. Moreover,
VITO representations are significantly more robust to natural and synthetic
deformations than image-, video-, and adversarially-trained ones. Finally,
VITO's predictions are strongly aligned with human judgements, surpassing
models that were specifically trained for that purpose. Together, these results
suggest that video pretraining could be a simple way of learning unified,
robust, and human-aligned representations of the visual world.Comment: Technical repor
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