Do Vision Transformers See Like Convolutional Neural Networks?

Convolutional neural networks (CNNs) have so far been the de-facto model for visual data. Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or even superior performance on image classification tasks. This raises a central question: how are Vision Transformers solving these tasks? Are they acting like convolutional networks, or learning entirely different visual representations? Analyzing the internal representation structure of ViTs and CNNs on image classification benchmarks, we find striking differences between the two architectures, such as ViT having more uniform representations across all layers. We explore how these differences arise, finding crucial roles played by self-attention, which enables early aggregation of global information, and ViT residual connections, which strongly propagate features from lower to higher layers. We study the ramifications for spatial localization, demonstrating ViTs successfully preserve input spatial information, with noticeable effects from different classification methods. Finally, we study the effect of (pretraining) dataset scale on intermediate features and transfer learning, and conclude with a discussion on connections to new architectures such as the MLP-Mixer.

Pointer Value Retrieval: A new benchmark for understanding the limits of neural network generalization

The successes of deep learning critically rely on the ability of neural networks to output meaningful predictions on unseen data -- generalization. Yet despite its criticality, there remain fundamental open questions on how neural networks generalize. How much do neural networks rely on memorization -- seeing highly similar training examples -- and how much are they capable of human-intelligence styled reasoning -- identifying abstract rules underlying the data? In this paper we introduce a novel benchmark, Pointer Value Retrieval (PVR) tasks, that explore the limits of neural network generalization. While PVR tasks can consist of visual as well as symbolic inputs, each with varying levels of difficulty, they all have a simple underlying rule. One part of the PVR task input acts as a pointer, giving the location of a different part of the input, which forms the value (and output). We demonstrate that this task structure provides a rich testbed for understanding generalization, with our empirical study showing large variations in neural network performance based on dataset size, task complexity and model architecture. The interaction of position, values and the pointer rule also allow the development of nuanced tests of generalization, by introducing distribution shift and increasing functional complexity. These reveal both subtle failures and surprising successes, suggesting many promising directions of exploration on this benchmark.

Deduplicating Training Data Makes Language Models Better

We find that existing language modeling datasets contain many near-duplicate examples and long repetitive substrings. As a result, over 1% of the unprompted output of language models trained on these datasets is copied verbatim from the training data. We develop two tools that allow us to deduplicate training datasets -- for example removing from C4 a single 61 word English sentence that is repeated over 60,000 times. Deduplication allows us to train models that emit memorized text ten times less frequently and require fewer train steps to achieve the same or better accuracy. We can also reduce train-test overlap, which affects over 4% of the validation set of standard datasets, thus allowing for more accurate evaluation. We release code for reproducing our work and performing dataset deduplication at https://github.com/google-research/deduplicate-text-datasets.

Understanding invariance via feedforward inversion of discriminatively trained classifiers

A discriminatively trained neural net classifier achieves optimal performance if all information about its input other than class membership has been discarded prior to the output layer. Surprisingly, past research has discovered that some extraneous visual detail remains in the output logits. This finding is based on inversion techniques that map deep embeddings back to images. Although the logit inversions seldom produce coherent, natural images or recognizable object classes, they do recover some visual detail. We explore this phenomenon further using a novel synthesis of methods, yielding a feedforward inversion model that produces remarkably high fidelity reconstructions, qualitatively superior to those of past efforts. When applied to an adversarially robust classifier model, the reconstructions contain sufficient local detail and global structure that they might be confused with the original image in a quick glance, and the object category can clearly be gleaned from the reconstruction. Our approach is based on BigGAN (Brock, 2019), with conditioning on logits instead of one-hot class labels. We use our reconstruction model as a tool for exploring the nature of representations, including: the influence of model architecture and training objectives (specifically robust losses), the forms of invariance that networks achieve, representational differences between correctly and incorrectly classified images, and the effects of manipulating logits and images. We believe that our method can inspire future investigations into the nature of information flow in a neural net and can provide diagnostics for improving discriminative models.

On Deep Learning with Label Differential Privacy

In many machine learning applications, the training data can contain highly sensitive personal information. Training large-scale deep models that are guaranteed not to leak sensitive information while not compromising their accuracy has been a significant challenge. In this work, we study the multi-class classification setting where the labels are considered sensitive and ought to be protected. We propose a new algorithm for training deep neural networks with label differential privacy, and run evaluations on several datasets. For Fashion MNIST and CIFAR-10, we demonstrate that our algorithm achieves significantly higher accuracy than the state-of-the-art, and in some regimes comes close to the non-private baselines. We also provide non-trivial training results for the the challenging CIFAR-100 dataset. We complement our algorithm with theoretical findings showing that in the setting of convex empirical risk minimization, the sample complexity of training with label differential privacy is dimension-independent, which is in contrast to vanilla differential privacy.

What is being transferred in transfer learning?

One desired capability for machines is the ability to transfer their knowledge of one domain to another where data is (usually) scarce. Despite ample adaptation of transfer learning in various deep learning applications, we yet do not understand what enables a successful transfer and which part of the network is responsible for that. In this paper, we provide new tools and analyses to address these fundamental questions. Through a series of analyses on transferring to block-shuffled images, we separate the effect of feature reuse from learning low-level statistics of data and show that some benefit of transfer learning comes from the latter. We present that when training from pre-trained weights, the model stays in the same basin in the loss landscape and different instances of such model are similar in feature space and close in parameter space.

What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation

Deep learning algorithms are well-known to have a propensity for fitting the training data very well and often fit even outliers and mislabeled data points. Such fitting requires memorization of training data labels, a phenomenon that has attracted significant research interest but has not been given a compelling explanation so far. A recent work of Feldman (2019) proposes a theoretical explanation for this phenomenon based on a combination of two insights. First, natural image and data distributions are (informally) known to be long-tailed, that is have a significant fraction of rare and atypical examples. Second, in a simple theoretical model such memorization is necessary for achieving close-to-optimal generalization error when the data distribution is long-tailed. However, no direct empirical evidence for this explanation or even an approach for obtaining such evidence were given. In this work we design experiments to test the key ideas in this theory. The experiments require estimation of the influence of each training example on the accuracy at each test example as well as memorization values of training examples. Estimating these quantities directly is computationally prohibitive but we show that closely-related subsampled influence and memorization values can be estimated much more efficiently. Our experiments demonstrate the significant benefits of memorization for generalization on several standard benchmarks. They also provide quantitative and visually compelling evidence for the theory put forth in (Feldman, 2019).

Exploring the Memorization-Generalization Continuum in Deep Learning

Human learners appreciate that some facts demand memorization whereas other facts support generalization. For example, English verbs have irregular cases that must be memorized (e.g., go->went) and regular cases that generalize well (e.g., kiss->kissed, miss->missed). Likewise, deep neural networks have the capacity to memorize rare or irregular forms but nonetheless generalize across instances that share common patterns or structures. We analyze how individual instances are treated by a model on the memorization-generalization continuum via a consistency score. The score is the expected accuracy of a particular architecture for a held-out instance on a training set of a fixed size sampled from the data distribution. We obtain empirical estimates of this score for individual instances in multiple datasets, and we show that the score identifies out-of-distribution and mislabeled examples at one end of the continuum and regular examples at the other end. We explore three proxies to the consistency score: kernel density estimation on input and hidden representations; and the time course of training, i.e., learning speed. In addition to helping to understand the memorization versus generalization dynamics during training, the C-score proxies have potential application for out-of-distribution detection, curriculum learning, and active data collection.

Identity Crisis: Memorization and Generalization under Extreme Overparameterization

We study the interplay between memorization and generalization of overparametrized networks in the extreme case of a single training example. The learning task is to predict an output which is as similar as possible to the input. We examine both fully-connected and convolutional networks that are initialized randomly and then trained to minimize the reconstruction error. The trained networks take one of the two forms: the constant function ("memorization") and the identity function ("generalization"). We show that different architectures exhibit vastly different inductive bias towards memorization and generalization. An important consequence of our study is that even in extreme cases of overparameterization, deep learning can result in proper generalization.

Transfusion: Understanding Transfer Learning with Applications to Medical Imaging

With the increasingly varied applications of deep learning, transfer learning has emerged as a critically important technique. However, the central question of how much feature reuse in transfer is the source of benefit remains unanswered. In this paper, we present an in-depth analysis of the effects of transfer, focusing on medical imaging, which is a particularly intriguing setting. Here, transfer learning is extremely popular, but data differences between pretraining and finetuing are considerable, reiterating the question of what is transferred. With experiments on two large scale medical imaging datasets, and CIFAR-10, we find transfer has almost negligible effects on performance, but significantly helps convergence speed. However, in all of these settings, convergence without transfer can be sped up dramatically by using only mean and variance statistics of the pretrained weights. Visualizing the lower layer filters shows that models trained from random initialization do not learn Gabor filters on medical images. We use CCA (canonical correlation analysis) to study the learned representations of the different models, finding that pretrained models are surprisingly similar to random initialization at higher layers. This similarity is evidenced both through model learning dynamics and a transfusion experiment, which explores the convergence speed using a subset of pretrained weights.