Accelerating Augmentation Invariance Pretraining
Authors: Jinhong Lin
Cheng-En Wu
Yibing Wei
Pedro Morgado
Venue: NeurIPS 2024 poster (2024)
Keywords: Self-supervised learning
Vision Transformer
Accelerating training
Primary area: machine_vision
TL;DR: This paper explores accelerating self-supervised learning pretraining by leveraging the features of Vision Transformer.

Abstract: Our work tackles the computational challenges of contrastive learning methods, particularly for the pretraining of Vision Transformers (ViTs). Despite the effectiveness of contrastive learning, the substantial computational resources required for training often hinder their practical application. To mitigate this issue, we propose an acceleration framework, leveraging ViT's unique ability to generalize across inputs of varying sequence lengths. Our method employs a mix of sequence compression strategies, including randomized token dropout and flexible patch scaling, to reduce the cost of gradient estimation and accelerate convergence. We further provide an in-depth analysis of the gradient estimation error of various acceleration strategies as well as their impact on downstream tasks, offering valuable insights into the trade-offs between acceleration and performance. 
    We also propose a novel procedure to identify an optimal acceleration schedule to adjust the sequence compression ratios to the training progress, ensuring efficient training without sacrificing downstream performance. Our approach significantly reduces computational overhead across various self-supervised learning algorithms on large-scale datasets. In ImageNet, our method achieves speedups of 4$\times$ in MoCo, 3.3$\times$ in SimCLR, and 2.5$\times$ in DINO, demonstrating substantial efficiency gains.

Accelerating Augmentation Invariance Pretraining
Jinhong Lin" Cheng-En Wu"
Yibing Wei Pedro Morgado
University of Wisconsin Madiion
(jlin522, cwu356, wei96, pmorgado)@visc.eed
Abstract
Our work tackles the computational challenges of contrastive learning methods, particularly for the pretraining of Vision Transformers (ViTs). Despite the effee- tiveness of contrastive learning, the substantial computational resources required for training often hinder their practical application. To mitigate this issue, we propose an acceleration framework, leveraging ViT's unique ability to generalize across inputs of varying sequence lengths. Our method employs mix of sequence compression strategies. including randomized token dropout and flexible patch scaling, to reduce the cost of gradient estimation and accelerate convergence, We further provide an in-depth analysis of the gradient estimation error of various acceleration strategies as well their impact on downstream tasks, offering valu- able insights into the trade-offs between acceleration and performance. We also propose a novel procedure to identify an optimal acceleration schedule to adjust the sequence compression ratios to the training progress, ensuring efficient training without sacrificing downstream performance. Our approach significantly reduces computational overhead across various self-supervised learning algorithms on large-scale datasets. In ImageNet, our method achieves speedups of 4x in MoCo, x.3x in SimCLR, and 2.5x in DINO, demonstrating substantial efficiency gains.
Introduction
Self-supervised learning (SSL) has emerged as powerful pre-training paradigm, demonstrating remarkable success across a variety of domains. By designing pretext tasks that leverage unlabeled data, SSL eliminates the need for costly and labor-intensive manual annotations. Among SSL methods, contrastive [5, 16] and distillation-ba based [4, 24] learning are among the most effective. They learn representations through transformation invariance, meaning that the model learns to create similar representations for different augmentations of the same image, but different across images. These approaches have led to the development of state-of-the-art models for tasks ranging from image recognition [16, 12] to object detection [16] and video object segmentation [4].
Despite these achievements, SSL requires substantial computational resources for pretraining, with optimal performance often necessitating long training schedules, hindering their practical application. The computational demands of SSL are particularly high for Vision Transformers (ViTs), a promising class of neural networks that has recently gained significant attention for visual understanding tasks. ViTs represent images as sequences of patches, processed through self-attention layers. While self-attention enables the model to capture long-range dependencies and complex patterns more effectively than convolutional networks, their enhanced expressiveness (and weaker inductive bias) require even more extensive pre-training to achieve competitive performance. Our work aims to address these computational challenges by proposing an acceleration framework specifically tailored for ViTs.
Existing attempts to accelerate SSL pretraining primarily focus defining improved learning rates and data augmentation schedules for faster learning [19] or increasing the strength of supervision through multiple targets [8]. While beneficial, these methods are not tailored to the ViT architecture, and can only provide limited acceleration. They also often change the underlying pretraining
"qqual Contribution
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

---

50-
67.5
05.0
02.5
10.0
Accelested MoCo
Accileaoedd MaCo
Accelensted Mico
MoCs
NoCo
Accelerated MoCo
57.5
150
203
165
405
530
1:00
300
400
500
1050 2002 1003 4000 5002 2003 Jsed Sudgets (9)
1200 2000 1000 4500 5020 $000
Uooi Sudpes 190
Lood Budlass (N)
Used budget (M)
(a) ImageNet-100
(b) ImageNet-1k
Our accelerated MoCo-v3 achieves standard MoCo-v3 performance using only 1/5 of the training budget on ImageNet- 100 and on ImageNet-IK, Ik. The training budget (x-axis) is measured as the training time normalized by the forward pass of the hase non-accelerated hackhone model, in million (M) units. The results for ImageNet- 100 are shown in Fig. 1a and for ImageNet-1k l in Fig. 1b.
Figure 1:
algorithm, making of additional losses data augmentations. Instead, investigate acceleration techniques that leverage the ViT's unique ability to generalize across inputs of varying sequence lengths, while faithfully preserving the model's architectural design.
Since the time complexity of training iteration is proportional the input sequence length, our method identifies at each moment in time the most cost-effective mix of two simple sequence compression strategies: (I) randomized token dropout and (2) flexible patch scaling. We show that when applied judiciously with appropriately optimized schedule, these simple strategies can significantly reduce the cost of gradient estimation, leading to faster convergence without compromising the quality of the learned representations (see Fig. 1). Our approach is general and can be applied to a wide range of SSL pre-training algorithms, as it only modifies the input sequence of the VIT. To demonstrate its efféctivvness and generality,
apply our method for MoCo-V3 [16]. SimCLR [5] and DINO [4], achieving significant training speed-ups on standard pre-traing tre-training datasets like ImageNet- IK (between 2.5 to 4 times faster than the original methods). Additionally, we conduct a series of experiments on MoCo-VS V3 to perform a deeper and more general analysis of the proposed acceleration strategies. Through the trade-offss between acceleration and performance, and the intricacies of establishing an optimal acceleration schedule. Specifically, we (1) investigate the gradient estimation error of various acceleration strategies alongside their performance on downstream tasks, study the impact of (2) compression rates query and target sequences for contrastive learning, and (3) varying training budgets (showing that constant compression fails to meet peak model performance), and (4) establish an optimal acceleration schedule that adjusts the training progress by minimizing the expected error of gradient estimation. Our analysis shows that, while the early phases of training typically benefit from aggressive acceleration strategies with high token dropout rates or large patch sizes, the gradient estimation biases increase as the model converges. Consequently, the optimal strategy should gradually shift towards smaller patches and lower dropout ratios.
analysis, we provide insights into
2 Related Work
Representation Learning Through Transformation Invariance involves training models to produce consistent representations for augmented versions of the same image, primarily via contrastive learning and distillation methods. Contrastive learning achieves this by contrasting positive pairs, generated from augmenting the same image, with negative pairs from different images, as explored in numerous studies [13, 7, 6, 32, 23, 17, 1, 16, 5, 4]. Distillation methods [4, 24] focus aligning embedding distributions across augmentations of varying scales without relying on negative samples. Both approaches effectively enhance feature quality without the need for labeled data.
Accelerating Augmentation Invariance Pre- Tre-Training Despite the high computational requirements. accelcrating pre-training of vision transformers has remained underexplored. A few strategies have nevertheless been proposed. While focusing on ResNets, [19] introduced an architecture- agnostic method that dynamically adjusts augmentation intensity and learning rate schedules to hasten the training process. Some works focus on ViTs but modify its architecture or the underlying algorithm for acceleration. For example, [21] progressively merges tokens within the model, and [8] utilizes multiple small crops as positive pairs to increase the strength of supervision and promising convergence with smaller training budgets. In contrast, we propose novel acceleration procedure for identifying the optimal schedule of simple sequence compression strategies, by ensuring that gradient estimation is cost-effective without introducing significant estimation biases.

---

Query View
Zq
Compressed Tokenizer
Compressed Tokenizer
Encoder
Random Token Dropout
X
Loss
Flaxible Fatch Scaling
Compressed Tokenizer
Encoder
X
Key View
Z
Potch Embedding Weights
Figure 2: Framework overview. We propose method for accelerating augmentation invariance pre-training of transformer neural networks. Acceleration is achieved by compressing the ViT's input sequence length using two strategies: (I) randomized token dropout and (2) flexible patch scaling. We further introduce gradient error analysis framework to assess the efficacy of an acceleration strategy, enabling us to define an optimal acceleration schedule that adjusts to the training progress. The acceleration strategy can be applied to variety of methods. For example, SimCL. optimizes both encoders hy gradient descent, while MoCo and DINO use momentum encoder compute the representations for the Key view. The loss function also differs across algorithms.
Accelerating Model Training The computational demands of pretraining extend beyond contrastive learning. Various strategies have been proposed to mitigate this, such as random masking, a technique used in Vision Language pretraining to scale up training [22], and masked image modeling [14, 2] for in-context reconstruction. Other acceleration techniques include curriculum learning, starting with simple samples and progressively moving harder ones [34, 26]; flexible ViT architectures that gradually increase in depth and width [25]; and resolution scaling, using low-resolution inputs at initial stages for faster convergence [20, 27, 30].
While these methods have been successful accelerating pre-training, they often require changes to the model architecture or the underlying training algorithm. They have also been primarily designed for variety of tasks beyond contrastive learning, and thus their findings and proposed algorithms may not be directly applicable. Our work covers this gap by proposing novel acceleration framework tailored for augmentation invariance pre-training of ViTs. Furthermore, while the building blocks (token dropout and patch scaling) of our acceleration framework are closcly related to existing methods, our work is the first to systematically analyze their impact on gradient estimation and to leverage this analysis to define acceleration schedule that adjusts the training progress.
[Background] Augmentation Invariance Pre-Trainine of ViTs
We present an approach to accelerate augmentation invariance pre-training of Vision Transformers. Since the proposed strategy only modifies the input sequence, it is applicable to wide range of algorithms. For comprehensive empirical analysis, we apply our method to MoCo-V3 [7], SimCLR [5] and DINO [4].
3.1 Augmentation Invariance Pre-Training
While contrastive learning and distillation-based methods may differ in their implementation, they generally rely on augmentation invariance as the source of supervision. Given an input image æ, two views generated using random data augmentation procedure 'T and often referred as the query FG = T(x) and the key 2g = Txx). These are processed by two encoders fg and fk- producing query and key n-dimensional representations 2y (xgg)) E Rº and 21: (Ira) E R" Augmentation invariance is encouraged by aligning the query 2, and key 24 representations of the same image. Two major differences between algorithms are: the choice of the key encoder fx and the loss function leveraged to impose augmentation invariance.
Key Encoder While SimCLR uses a shared encoder to encode both views (i.e., fa f MoCo-Vi V3 and DINO use momentum encoder [28] for the keys Zg- Momentum encoders f& share the architecture, but their parameters are computed as the exponential moving average of the online encoder fq. Momentum encoders have been shown to yield more stable target representations, and consequently improved representation learning.
Loss function Augmentation invariance be enforced through variety of loss functions. Among the chose algorithms, both SimCLR and MoCo-V3 leverage the InfoNCE loss [23]
exp (sim (Zq, xx)) /
LINONNCE
loo exp
(1)
(sim (zq: xx) /T) Σx, EZ, exp (sim (zq 2x) /7)
3

---

= the corresponding where sim (:--) is the cosine similarity, T temperature parameter, 2g and 2g query and key representations, and 2x set of negative keys obtained from other images in the same batch. Unlike MoCo-V3 and SimCLR, DINO uses a distillation loss instead, which seeks to align query and key representations (also referred to student and teacher's representations) without the explicit use of negatives samples. To accomplish this, query and key representations are first converted into probability vector through softmax operator p SoftMax(z/T) E R", and the model is trained to minimize the cross-entropy between the two
Ldist =
Σ
Pas Pq pq.
(2)
The sum is taken over the dimensions of the probability vector.
Gradient Acceleration Through Sequence Compression
While augmentation invariance can be used to learn representations with any type of neural network, the proposed acceleration procedure leverages properties specific to transformer models. In this work, we focused on Vision Transformers (ViTs) [10], widely used architecture for vision tasks. ViTs process an image x: of resolution H x W by dividing it into grid of (H/ /p, W//p) patches, each of size p p.p. After embedding each patch into a d-dimensional vector and adding positional encodings to mark their spatial location, the VIT processes the sequence of patch embeddings through several self- attention transformer blocks [31]. Since transformer blocks' parameters are shared across the input sequence, the gradients of the loss wrt its parameters can be computed regardless of input sequence lenght. this work, we tackle two key questions: (1) How to reduce the input sequence length with limited impact the gradient estimation error? and (2) How to characterize the effectiveness of an acceleration strategy? We introduce two strategies, randomized token dropout and dynamic patch scaling, and propose methodology to determine the effectiveness of gradient acceleration of a given strategy by analyzing its cost-adjusted bias- variance trade-off. This methodology allows us to identify the optimal acceleration strategy at each moment during training, eliminating the need for manual hyper-parane parameter tuning.
4.1 Randomized Token Dropout
Randomized token dropout (TknDrop) is simple strategy for reducing the sequence length in ViTs, by simply removing a random subset of tokens from the input sequence. This strategy is especially effective in vision, since neighboring pixels are highly correlated and the model can still infer the visual content from partial view of the image. However, while highly compressed sequences can speed up gradient estimation, too much compression may cause significant biases in the estimated gradients and consequently degraded model performance. Determining the optimal dropout rate is thus crucial for effective acceleration.
TknDrop is inspired by MIM methods [15, 2]. which also mask the input sequence. However, while MIM uses masking to establish reconstruction targets for representation learning, we leverage token dropout to generate compressed input sequences to accelcrate augmentation invariance pre-training.
4.2 Patch Scaling
The second strategy involves splitting the input image into coarser grid of patches. As the sequence length L is inversely proportional to the patch size p. larger patches allow us to reduce without removing any pixels from the input. However, since the patch embedding layer W depends on a predefined patch size p. larger patches cannot be directly encoded. To mitigate this issue, we leverage the flexible patch embeddings introduced in [3], where W Wpucc are dynamically resized to accommodate different patch sizes. Consider the weights wp- E 32 of single output dimension of W Wparch. Instead of simple interpolation, the optimal weights wy E अर at the larger size q are computed by finding a projection w, Puc that minimizes the distance between the embedding of the original patch Tp and the interpolated larger patch xp--q- Specifically, the optimal projection P is obtained by solving
R2 - R
arg min Ex, ((zp. - (xp-gg pwp))
(3)
where the expectation is taken over a distribution of patches Xx.

---

Method Sample Cost (C)
1.54
1.44
1.35
1.25
1.19
40
1.71
1.57
1.43
1.29
1.20
Tig + T.k
SimCLR
31.g Inwag
Patch
30
2.14
1.89
64
39
1.25
2
69
2
30
9
1.53
1.30
MoCo-v3
Liser
20
3.36
2.80
2.25
1.70
1.37
31g 3K 1i + I.k
DINO
16
4.59
3.73
2.87
2.01
1.49
Libew
0.0
0.25
0.5
0.75
0.9
Table 1: Hardware- independent sample cost of different pre- pre-training algorithms. We assume rel- atively short sequence lengths (typical of pre- training frameworks) where linear operations dom- inate over the quadratic self-attention operations.
Token Dropout Rate
Accelerated MoCo-v3 sam- ple for varying dropout rates and patch sizes. We assume uncompressed key sequenees.
Figure 3:
4.3 Combined Sequence Compression
Large patches can be trivially combined with token dropout by applying the two strategies in sequence. The sequence length are thus modulated by the selected patch size q and token dropout rate d. Specifically, an image of size H x W split into grid of pxp patches yields an uncompressed sequence of lenght L = HW After compression, the sequence lenght is lowered to L = =(1 (1- (1-d) d) x HW
4.4 Quantifying acceleration
Linear complexity assumption. Transformer blocks use two types of operations. token-wise trans- formations and self-attention. Token-wise operations, such as the MLP block or the query/key/value heads in self-attention, process each token in the sequence independently and thus scale linearly (L) with its length L. Self-attention operations, on the other hand, establish relationships between all pairs of patches and thus scale quadratically (L However, the sequence length for most model pre-training frameworks is relatively small (tipically I. 197). Since there are many more linear operations than quadratic ones, the time complexity of linear operations dominates this scale. Empirically, we observed that quadratic operations only become significant when the sequence length exceeds 400 patches. Thus, for the sake of simplicity, we assumed the time complexity ViT pre-training to be linear with L.
Sample costs of various algorithms. Let the time spent per token be denoted tan- Then, under the linear complexity assumption, a forward pass takes approximately tnw L tak. seconds, and backward pass twice long, fnnd 2L x fimm as both the partial derivatives wrt the latent representations and the model parameters need to be computed [18]. Since, for SimCLR, the backward pass performed both encoders, the total time per sample is ump = 3( LA Xtt For MoCo-v3, the backward pass is only performed on the query encoder, and thus Camp (3L, + Ls) x Lik. DINO also uses K smaller augmentations as additional query sequences for its distillation loss, further increasing the sample time to tmp = (3L. 3K L !itiill + Lb) x fin. Finally, hardware dependencies (captured through tgg)) can be removed by normalizing tamp by the forward pass of standard input thave = T-hase X lakn. where Thhaee 197 is the sequence length for regular 14x 14 grid. Hardware independent sample costs are summarized in Table 1.
As can be seen, regardless of pre- pre-training algorithm, the sample cost is proportional to the sequence lengths, Ly and Lą- To visualize the impact of compression, show the sample costs with varying token dropout ratios and patch sizes for MoCo-v3 in Fig. 3. These cost assume an uncompressed key sequence,
empirically found that model pre-training is often more effective when supervision targets are computed without compression'. As can be seen, speed ups as large as 4x can be achieved with 90% dropout rates and patches of size q 48. However, usefull acceleration strategies should not only reduce the sample cost, but also minimize their impact on the estimated gradients.
5 Gradient Estimation Analysis of Acceleration Strategies
Given the large search space, empirically selecting the most effective strategy at each stage of training is computationally prohibitive. Instead, posit that the distribution of the accelerated gradients should closely resemble that of the non-accelerated model. This criterion, further expanded below, can be used to inform us of the optimal mix of accelerated strategies at any point throughout training.
Crrops of size 240 240 were used, this resolution is divisible by larger of patch sizes.
5

---

5.1 Formulation
Gradient Distribution in Mini-Batch Training Model optimization requires the minimization of loss function ((x; 0) wrt the model parameters 0. Assuming independence between samples æ in the training dataset D, the expected loss is given by
(()) BE-D (ar; 0)] ~
1 [8]
E 0)
(4)
where [B] is the mini-batch size. Optimization algorithms update the model parameters 0 using the gradient of the loss.
V VgC(O) = Dg Vol(<; 0) ~ [8
Eier Vol(x,;0)
(5)
For simplicity, denote the sample gradient go (xx) (a, 0), and the true gradient (computed over the entire dataset) as G2 = VoC( Since samples are independently drawn from the training dataset D, then the sample gradient (z)) is random variable with mean and covariance given by
E [g)) Ga and
Cov [go()))] IE (x) Go) (go(a)) Ga)
(6)
Similarly, batch gradients are also unbiased estimates of the true gradient but with variance reduced by factor of [B]
E
(0(x))
Ge and Cov
1
Σ (x) .
Cov [ge (z)]
B
B
EEB
Gradient Estimation Errors When assessing an acceleration strategy, we need to consider both the mean and variance of the gradient estimates. While the bias is intrinsic property of each strategy, the variance is a function of their computational cost. Strategies that reduce the computational cost significantly can potentially be used average the gradients over larger number of samples and thus reduce their variance. Thus, to fairly capture the bias-variance tradeoff' of cach strategy, we propose use the Mean Squared Error (MSE) of the gradient estimate obtained with a cost adjusted batch size. From parameter estimation theory, it can be easily shown that the sample MSE decomposes into (squared) bias and variance components
MSE (g)( Go)
E |1gH (ax) -Gol
(8)
2 G.oo -00--1112 + IE [108(ar) 5812
Bias Bias" Go) + Var(gE)
where 96 E [x] is the average accelerated gradient using strategy In other words, the MSE accounts for both the average deviation of the accelerated gradients from the ground truth and their variance across samples. Adjusting the MSE score for the cost of each strategy simply requires adjusting the variance by the number of samples within a fixed budget.
Cost(c)
CA-MSE(gi) Bias? (g6. Go)
Var (96).
(10)
Budget
Estimating Bias and Variance Both the bias and variance components can be efficiently computed given model with parameters 9 and dataset D. Gg and T6 can be approximated by the sample average of the non-accelerated and accelerated gradients, respectively. While automatic differentiation libraries only track the aggregated gradient during batch processing, preventing from directly computing the sample variance, we can estimate it by dividing the batch into smaller sub-batches (of size K) and adjusting the variance across sub- sub-batches by K. Finally, it should be noted that obtain a reliable estimate of CA-MSE, we need a large enough number of samples (we used 16k samples).
Dynamic Acceleration The cost-adjusted MSE provides a means to compare acceleration strategies without full training and evaluation. This metric could also be used to select the most effective strategy on-the-fly, at different stages during the training process. However, in practice, we conducted our analysis on intermediate checkpoints of a pre-trained model and used the findings to establish the "CA-MSE optimal" acceleration schedule for each method.
5.2 Analysis
In Section 4, we introduced two strategies to reduce the input sequence length (randomized token dropout and patch scaling) thereby reducing the cost of estimating the gradients of the model,

---

Training Progress
50
100
CA MSEM
Dropoutt
111
938
1.44
10
5M
CUM CAT
C31
1.2
123
339
15
0.6
1:01
11B 1:15
1000
61B
1.4
1004
04.4
100 12.1 A21
+44
2.4
Dropout
8:21
0.5
1.3
L14
322 0344
GAR
037
LAN
629
4.2
Bias
8:0
1.0
X12 9.4
3:5
L00
607
033
028
004
638
629
065
0.0
130
101
x16 9.3
1:6
0.3
L:4
301
012
0.2
6:0
0.3
0.5
622
(3)
Patch
25
200
-ar
Dropout
144
1:24
0.0
076
0.0 0.9
674
0.04 9.44
114
9:1
5:0 1:4
PM
235
X42
340
887
6.1 6:11
038
328
5.5
0 21
2
633
329
332
6.4
054
025
0.24
0.2
9:25
31
639
48
48
15:
30
40
46
20:
24
10
15
Figure 4: Error profile of accelerated gradients. From top bottom, the three panels show the CA-MSE, squared bias and adju-adj variance of the gradient estimates, using different acceleration strategies and different stages of training.
potentially at the expense of inaccurate gradients. To investigate their cost-accuracy trade-off, we measured the cost-adjusted MSE 5 different stages of training progress, 0%, 25%, 50%, 75% and 100% of training progress. We varied the dropout ratio in the set {0, 0.25, 0.5, 0.75, 0.9) and the patch size in {16, 20, 24, 30, 40, 48). Fig. 4 presents the CA-MSE score, normalized by the magnitude of the ground-trune gradient, across all configurations. Early in training (as seen in the first panel), both non- acceecceerrted gradients (i.e.. 0% dropout and patch size 16) and gradients derived from highly compressed input sequences (high dropout ratios and large patches) exhibit high MSE. However, non-accelerated gradients show low bias and high variance, while highly compressed gradients show high bias but low variance. More favorable trade-offs are achieved by combining moderate compression using both strategies simultaneously. As training advances and the model converges (remaining panels), the optimal strategy gradually shifts towards smaller patches and lower dropout ratios. This shift occurs because, as the model converges, gradient magnitudes shrink and the MSE becomes more sensitive to estimation biases.
Experiments
To assess the impact of acceleration model performance, we conducted extensive exploration and ablation experiments, using the MoCo-V3 pre-training framework. We also assessed the generaliz- ability of our methodology by accelerating other frameworks like DINO and SimCLR.
6.1 Experimental Setup
Dataset We conduct all experiments on the ImageNet dataset [9] using a ViT-Base transformer backbone for both the online and target encoders. Ablations and parametric studies are conducted on the ImageNet- 100 (IN100) dataset, a randomly chosen subset of 100 classes from ImageNet. We adhere to the class partitioning used in previous studies [33, 29]. With around 125,000 images, IN100 provides a substantial amount of data for conducting statistically meaningful experiments. We also validate our findings on the full ImageNet- Ik dataset to ensure the generalizability of our results.
Downstream Evaluation We assess the quality of the learned representations in three ways. In line with standard practices in self-supervised learning, we measure the classification accuracy on the pre-training dataset either using linear probe (LP) with frozen features after full model finetuning (FT). We also measure the nearest neighbor accuracy (NN) as an indicator of the effectiveness of the learned representations. All downstream evaluations are conducted without sequence compression. Pre-training settings To ensure fair reproduction, we followed the official implementations. In the case of MoCo, the only modification was the use of non-symmetric loss. Originally, the two augmentations 2yg and iy are used both as queries and targets, forming two pairs for each sample. However, this is equivalent to using only one pair, while doubling both the batch size and number
7

---

Polynomial Cosine Anneallig
Training
Dataset
Batch Size
Epochs
Training Budget
NN
L.
FT
0.10
IN-100
512
600
200
312M
10HM
68.7
58.1
80.4
75.8
90.4
88.0
Leamnin Rate
0.08
520M
70.0
90.6
0.06
100
1028M
50.3
70.3
82.0
IN-IK
1024
3084M
59.1
0.04
600
6168M
61.9
76.0
82.5
IN-IK
300
6168M
83.2
2000
4:000
6000
8000
10000
Figure 5: Non-accelerated MoCo-v3 across training budgets and datasets, and comparison to the pub- lically released MoCo-Va V3 model (last row). The effective training epochs for the official MoCo imple- mentation is doubled, as it uses symmetric loss.
Iteration
bir
lsss Fasnssp Fevermpp
Figure 6: Cosine vs Polynomial (a = 2) learning rate decay schedules.
of epochs. The non-symmetric version also produces more diverse batches, which is advantageous given the use of batch normalization in the prediction heads.
To establish optimized baseline ImageNet- 100, empirically search for the learning rate, batch size and the required training budget (default values were used for other hyper-parameters) We observed that performance saturated for batch sizes of 512, and training budgets equivalent to 1000 epochs with a 40-epoch warmup phase. The optimal base learning rate was 5 x 10-2, adjusted by the batch size scaling rule [11].
As for ImageNet- 1k experiments, we followed the official training hyperparameters except for batch size, which was set to 1024 due to hardware limitations. MoCo's 's baseline performance on both ImageNet- and ImageNet-I are shown Fig. 5. As can be seen, despite the lower batch size, the model achieves comparable performance on ImageNet-1k (only 0.7% worse on both LP and FT accuracy).
Accelerated MoCo Pre-training As different gradient acceleration settings decrease the computa- tional cost of single iteration by different factors (see Fig. 3), fair comparisons require controlling the training budget, rather than the number of epochs. We express the training budget in units of the hardware independent sample costs defined in Section 4.4. On the ImageNet- 100 dataset, where optimal performance was achieved at training budget of 520M units (equivalent to 1000 epochs), we experiment with accelerated budgets ranging from 25M 200M units. On ImageNet- 1k. where baseline performance is achieved with budget of 6168M, we varied the accelerated budget between 300M and 1500M.
Similarly to [19], we observed that the learning rate schedule also impacts the effectiveness of acceleration strategies. Augmentation invariance pretraining commonly employs cosine decay schedule, which rapidly decreases in the second half of training. However, under constrained training budgets. this rapid decay hinders the model's ability to learn during the late stages. To address this, we use a polynomial decay schedule (see Fig. 6) to maintain a relatively higher learning rate in the later stages of training.
6.2 Constant Gradient Acceleration Strategies
We begin by assessing the representations obtained with each gradient accelcration strategy when applied uniformly and independently throughout training, using the MoCo-v3 pre-training framework. Randomized Token Dropout We studied the impact of token dropout on the learning process. To assess the efficacy of this approach, we trained the model with varying dropout rates for the query and key sequences, adhering to a restricted training budget of 100M units (20% of the budget utilized for the optimal MoCo setup). The results detailed in Table 2a support three noteworthy observations. First, with randomized token dropout, it is beneficial to keep the key (target) sequence uncompressed to preserve maximum information when calculating the targets for the query (online) encoder. We refer to this strategy as asymmetric acceleration. Second, training MoCo-v3 without acceleration under the constrained training budget (as shown in the last row of Table 2a) yields significantly inferior results compared to any of the tested accelerated versions. For example, acceleration via asymmetric token dropout with L, = 50 surpasses the non-accelerated model by 11.5% in NN accuracy and 4.0% in LP accuracy. This finding highlights the effectiveness of token dropout for accelerating the learning process. Finally, it is possible to compress the sequence too much as evidenced by the performance degradation when using Ly 20 (90% dropout rate). This result is
8

---

16
225
4.59
53.3
73.6
20
2.94
64.1
L,
L
Cost
NN
LP
24
100
2.04
67.1
81.2
58.2
40
36
0.73
62.0
76.3
20
40
0.5
59.2
74.9
197
63.9
60
50
65.4
(b) Sym patch scaling.
KNN Accuracy 10.
50
100
1.3
67.0
78.7
-50
69.6
100
100
2.0
59.8
70.0
P4
Ph
Cost
NN
40
Patch Sizes
197
2,5
69.3
$1.4
30
20
1.7
53.8
77.3
30
Patch Size 40
197
197
4,0
58.1
77.1
30
Patch Size 30
30
30
68.1
80.5
24
Size 20
(a) Sym and asym Tkn- Drop
10
Size 16
(c) Asym patch scaling.
0
40
60
80
100
Used Budget (M)
Table 2: Ablation studies of symmetric and asym- metric token dropout and patch scaling (training budget: 100M).
Figure 7: Training curves using constant symmet- ric patch scaling (training budget: 100M).
consistent with the observations from the gradient error analysis (Fig. 4), which shows large gradient estimation biases for such high dropout rates.
Patch Scaling We also examined the impact of patch scaling on learned representations, maintaining a training budget of 100M units. We tested symmetric and asymmetric compression strategies for query and key sequences. For this, we used 240 240 resolution images to accommodate patches of size 16, 20, 24, 30, and 40, resulting in an uncompressed sequence length of 225 (with = 16).
The results, in Tables 2b and 2c, mirror the findings of token dropout. Models trained with larger patch sizes expedite learning. outperforming the non-accelerated model (p = 16) within the same budget, as shown in Fig. 7. Too much acceleration, with patches scaled above 30 pixels, can however degrade performance, which is aligned with the increased bias observed in the gradient error analysis. However, unlike token dropout, symmetric patch scaling, where both the query and key sequences are equally compressed, is more advantageous (see Table 2c). This is likely because patch scaling modifies the distribution of the input patches, making it preferable maintain the same distribution for both the query and key sequences.
Accelerated Training Across Training Budgets The previous experiments showcased the efficacy of the proposed acceleration strategies within a constrained budget. To characterize their performance across an array of training budgets, we employed two representative strategies: asymmetric token dropout with Ly = 50 and La- = 197, and symmetric patch scaling with p = 30. We trained the model with increasing budgets, from 25M to 200M units, and evaluated their downstream performance. The results, shown in Table 3, unveil notable limitation of constant acceleration strategies. While these strategies are effective at lower budgets, they can overfit when the budget is increased. This especially evident in the case token dropout, as shown in Fig. As a result of this overfitting, although the proposed acceleration strategies can outperform the noc-acceler model at lower budgets, they fail to meet their peak performance. As suggested by the gradient error analysis in Fig. 4, this overfitting can be traced back to the increased biases of the accelerated gradients often witnessed in the final stages of training. This occurs because, as the model converges, the gradient strength diminishes, allowing biases introduced by the compressed sequences to exert greater influence on the learning process.
6.3 Optimized Acceleration Schedules
To circumvent the overfitting issue, we investigate optimized acceleration schedules that favor higher acceleration at the beginning and lower acceleration at the end of training. Although we can manually specify schedule based reasonable intuitions, the defined schedule would likely be suboptimal. Instead, we automate this process by leveraging the gradient error analysis of Fig. to establish a "CA-MSE optimal" acceleration schedule.
As expected, at the beginning of training, a high token drop ratio and larger patch sizes have lower cost-adjusted MSE. However, the training progresses, smaller patch sizes and lower drop ratios become more effective. We used these automatically derived schedules to train a model with three training budgets: 50M, 104M and 150M. As shown by the training curves in Fig. 8, by lowering the

---

Training Budget
TknDrop
PatchScale
1-0
NN
LP
NN
LP
25M
37.3
64.7
37.4
66.5
6.4 Accelerating Pretraining on ImageNet-lk Dropout Ratoon
Sicem32
50M
56.6
76.0
50.1
74.7
10
MoGo
Dynanic
75M
80.2
61.1
79.4
100M
69.6
81.2
66.7
80.5
100
200
520
4D0
500
Budge:
150M
68.2
80.8
68.9
82.1
200M
80.6
70.1
Figure 8: Training curve of three acceleration strategies: constant patch size. constant token dropout, and dynamic scheduling of joint patch scaling and token dropout.
Table 3: Impact of training budgets gradient acceler- ation strategies. Asymmetric token dropout (Ly = 50,
LA 197) and symmetric patch scaling (p 30).
acceleration towards the end of training. the model no longer overfits and is capable of reproducing the peak performance the MoCo-v3 baseline in less than 30% of the time.
Finally, to validate the gemeralizability of our findings, we deploy the proposed optimized ac- celeration scheduled dataset. Due to the unique characteristies of different SSL algorithms, we tailor our method slightly for each algorithm. As observed in 6.2, token dropout should be applied only the online encoder, while patch scaling should re- main consistent across encoders. However, since SimCLR directly optimizes over both encoders, treating them as online models, we found that its better to apply token dropout to both se- quences for SimCLR. ically found that the additional small crops used as queries are already compressed enough, and applying additional compression to these sequences is not beneficial.
Algorithm Accel.
Budget (M)
NN
LP
FT
the full ImageNet- 1k
1542
60.7
75.9
81.9
MoCo
6168
61.9
76.0
81.8
922
50.7
68.4
81.5
SimCLR
3075
50.2
68.3
81.3
1138
66.0
77.4
82.0
DINO
2846
67.3
77.4
81.8
Table 4: Acceleration of three augmentation invariance pretraining algorithms on ImageNet-IK. "Accel" indi- cated the of the optimized acceleration schedule.
for DINO, we empir-
The results, shown in Table 4, demonstrate that the dynamic acceleration strategy is capable of achieving competitive performance with the non-accelerated model, while significantly reducing the computational requirements for training. For instance, our method achieves comparable L.P accuracy for MoCo-V V3 (75.9% 76.0%) with only 25% of the original budget (1552M vs 61 68M iterations). We also achieved significant speedups for other pre-training frameworks, namely 2.5x for DINO and 3.3x for SimCLR.
Conclusion
In this paper, we propose general acceleration framework for self sufe-supeee learning that leverages simple sequence compression strategies to reduce the cost of gradient estimation. Our method is shown to significantly speed up the convergence variety of self-supervie supervieee methods, including constrastive (MoCo, SimCLR), and distillation-baser frameworks (DINO) thus demonstrating its broad usability.
Given the compute-intente intensiveeeevi nature of model pre-training and its implications on reproducibility. energy costs, and carbon footprints, we believe that further research on accelerated training is essential for advancing sustainable Al practices. Our paper aims to inspire continued exploration in this area, promoting the development of more efficient training methodologies that can (1) reduce the environmental impact of machine learning and (2) improve accessibility for researchers with limited resources.
References
[1] Bachman, P., Hjelm, R.D., Buchwalter, W.: Learning representations by maximizing mutual information across views. Advances on Neural Information Processing Systems (NeurIPS) 32 (2019) 2
10

---

[2] Bao, H., Dong, L.. Piao, S., Wei, F.: Beit: Bert pre-training of image transformers. In: Interna- tional Conference Learning Representations (ICLR) (2021) 3,4
[3] Beyer, L., Izmailov, P., Kolesnikov, A., Caron, M., Kornblith, S., Zhai, X., Minderer, M., Tschannen, M., Alabdulmohsin, I., Pavetic, F:: Flexivit: One model for all patch sizes. In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 14496-14506
(2023) 4
[4] Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: International Conference Computer Vision
(ICCV). pp. 9650-9660 (2021) 1, 3,
[5] Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (ICML). pp. 1597-1607. PMLR (2020)
[6] Chen, X., Fan, H., Girshick, R., He, K.: Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297 (2020) 2
[7] Chen, X., Xie, S., He, K.: An empirical study of training self-supervised vision transformers. In: IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR). pp. 1440-9649 (2021)
2,3
[8] Ci, Y., Lin, C., Bai, L., Ouyang, W.: Fast-moco: Boost momentum-based contrastive learning with combinatorial patches. In: European Conference on Computer Vision (ECCV). pp. 290-
306. Springer (2022) 1,2
[9] Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR). pp.
248-255. leee (2009) 7
[10] Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M. Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.1 19929 (2020) 4
[11] Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., He, K.: Accurate, large minibatch sgd: Training imagenet in hour. arXiv preprint (2017) 8
[12] Grill, J.B., Strub, F., Altché, F,, Tallec, C., Richemond, P., Buchatskaya, E., Doersch, C., Avila Pires, B., Guo, Z., Gheshlaghi Azar, M., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Advances on Neural Information Processing Systems (NeurIPS) 33, 21271-21284 (2020)
[13] Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: IEEE/CVE Conf. on Computer Vision and Pattern Recognition (CVPR). vol. 2, pp. 1735- 1742 (2006). http:://doi.org/// 109.0/.1//99/...0000 100 2
[14] He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR). pp.
16000-16009 (2022) 3
[15] He, K., Chen, X., Xie, S., Li, Y., Dollár, P., Girshick, R.: Masked autoencoders are scalable vision learners (2021)
[16] He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 9729-9738 (2020) 1,2
[17] Hjelm, R.D., Fedorov, A., Lavoie- Marchildon, S.. Grewal, K., Bachman, P., Trischler, A.,
Bengio, Y.: Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv: 001118888006777 (2018) 2
[18] Kaplan, J.: Notes on contemporary machine learning for physicists (2019) 5
[19] Koçyiğit, M.T., Hospedales, T.M., Bilen, H.: Accelerating self-supervised learning via efficient training strategies. In: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). pp. 5654-5664 (2023) 1, 2,
[20] Li, C., Zhuang, B., Wang, G., Liang, X., Chang, X., Yang, Y.: Automated progressive learning for efficient training of vision transformers. In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 12486-12496 (2022) 3

---

[21] Li. C., Yang, J., Zhang, P., Gao, M., Xiao, B., Dai, X., Yuan, L., Gao, J.: Efficient self-supervised vision transformers for representation learning, arXiv preprint arXiv:2106.09785 (2021)
[22] Li, Y., Fan, H., Hu, R., Feichtenhofer, C., He, K.: Scaling language-image pre-training via masking. In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 23390-23400 (2023) 3
[23] Oord, v.v.d., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. arXiv preprint 11XXvv1807.037444 (2018) 2,
[24] Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al.: Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.077
(2023) 1,2
[25] Pan, X., Jin, X., He, Y., Song, S., Huang, G., et al.: Budgeted training for vision transformer. In: International Conference Learning Representations (ICLR) (2022) 3
[26] Qin, Z., Wang. K., Zheng, Z., Gu. J., Peng, X., Xu, Z., Zhou, D., Shang, L., Sun, B., Xie, X., al.: Infobatch: Lossless training speed up by unbiased dynamic data pruning. arXiv preprint 2XXiv2333304944: (2023) 3
[27] Tan, M., Le, Q.: Efficienttetv2: Smaller models and faster training. In: International Conference on Machine Learning (ICML).. pp. 10096-10106. PMLR (2021)
[28] Tarvainen, A., Valpola, H.; Mean teachers are better role models: Weight-averaged consistency
targets improve semi-supervised deep learning results. In: Advances on Neural Information Processing Systems (NeurlPS), vol. 30 (2017)3
[29] Tian, Y., Krishnan, D., Isola, P:: Contrastive multiview coding. In: European Conference on Computer Vision (ECCV). pp- 776-794, Springer (2020) 7
[30] Touvron, H., Cord, M., Jégou, H.: Deit ii:: Revenge of the vit. In: European Conference on Computer Vision (ECCV). pp. 516-533 Springer (2022)
[31] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.,
Polosukhin, I:: Attention is all you need. Advances on Neural Information Processing Systems
(NeurIPS) 30 (2017) 4
[32] Wu, Z., Xiong, Y., Yu, S.X., Lin, D.: Unsupervised feature learning via non-parametric instance discrimination. In: IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR). pp.
3733-3742 (2018) 2
[33] Xiao, T., Wang, X., Efros, A.A., Darrell, T.: What should not be contrastive in contrastive learning. In: International Conference Learning Representations (ICLR) (2020)
[34] Zhou, T., Wang, S., Bilmes, Curriculum learning by dynamic instance hardness. Advances on Neural Information Processing Systems (NeurIPS) 33, 8602-8615 (2020) 3
12

---

NeurIPS Paper Checklist
The checklist is designed encourage best practices for responsible machine learning research, addressing issues of reproducibility. transparency, research ethics, and societal impact. Do not remove the checklist: The papers not including the checklist will be desk rejected. The checklist should follow the references and follow the (optional) supplemental material. The checklist does NOT count towards the page limit.
Please read the checklist guidelines carefully for information on how to answer these questions. For each question in the checklist:
You should answer [Yes], [No]. or [NA]-
[NA] means either that the question is Not Applicable for that particular paper or the relevant information is Not Available.
Please provide short (1-2 sentence) justification right after your answer (even for NA).
The checklist answers are an integral part of your paper submission. They are visible to the reviewers, area chairs, senior area chairs, and ethics reviewers. You will be asked to also include (after eventual revisions) with the final version of your paper, and its final version will be published with the paper.
The reviewers of your paper will be asked to use the checklist as one of the factors their evaluation.
While "[Yes] " is generally preferable to "[No] it is perfectly acceptable to answer " [No] proviided proper justification is given (e.g., "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). In general, answering "[No] or "[NA]" is not grounds for rejection. While the questions are phrased in binary way, we acknowledge that the true answer often more nuanced, so please just use your best, judgment and write justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] a question, in the justification please point to the section(s) where related material for the question can be found. IMPORTANT, please:
Delete this instruction block, but keep the section heading "NeurIPS paper checklist",
Keep the checklist subsection headings, questions/answers and guidelines below.
Do not modify the questions and only use the provided macros for your answers.
1. Claims
Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Question:
Answer: [Yes]
Justification:
Abstract and Introduction.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
is fine to include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [NA]
Justification: [NA]
Guidelines:
13

---

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create a separate "Limitations" section their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low images are taken in low lighting. Or speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community, Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory Assumptions and Proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [Yes]
Justification:
5 Gradient Estimation Analysis of Acceleration Strategies
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental Result Reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification: 6 Experiments
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data provided or not.
14

---

If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, other means that appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm. (b) If the contribution is primarily new model architecture, the paper should describe the architecture clearly and fully.
(c) If the contribution is a new model (e.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g.. with an open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g.. to registered users), but it should be possible for other researchers have some path reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [No]
Justification: The data (ImageNet1000 and ImageNet1000) are public. The code will be released after the paper accepted.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips cc/
Stwuiidde//ooduuumisssssessesese cnPolicyy) for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurlPS code and data submission guidelines (https:
/ mnips.cc/publl.. //ggguddsssss geuide//ooeeuumiiiiii Swtoe//oooiiiiiiiiiciieai ssionPolicy)) for more details.
The authors should provide instructions data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.
6. Experimental Setting/Details
15

---

Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary understand the results?
Question:
Answer: [Yes]
Justification:
6 Experiments
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented the core the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, supplemental material.
7. Experiment Statistical Significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [No]
Justification: Our experiments are time-consuming. is impractical repeat the experiment for error bars.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g., Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
It is OK to report 1-sigma eтror bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of enrors is not verified.
For asymmetric distributions, the authors should be careful figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).
show in tables or
If error bars are reported in tables or plots, The authors should explain the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments Compute Resources
For each experiment, does the paper provide sufficient information the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: 6 Experiments
Guidelines:
The answer NA means that the paper does include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
16

---

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
9. Code Of Ethics
Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: ftttps://neuripee httpppscccppbbb/eetieeeeeeeeeeee htt.lliicEhiicsccuudllieeeeeee
Question:
Answer: [Yes]
Justification: [NA]
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g.. if there is special consid- eration due to laws or regulations their jurisdiction).
10. Broader Impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: The techniques presented this paper can motivate more researchers with limited GPU resources engage in self-supervised learning.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA No, they should explain why their work has societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deploymeet of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied partieular applications, let alone deployments. However, if there is direct path any negative applications, the authors should point it out. For example, it is legitimate point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML..
11. Safeguards
Does the paper deseribe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: [NA]
Guidelines:
The answer NA means that the paper poses such risks.
17

---

Released models that have high risk for misuse or dual-use use should be released with necessary safeguards to allow for controlled use of the model. for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do not require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: All data and code used in this paper are cited appropriately.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website). the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswi thcode. com datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re- re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's 's creators.
13. New Assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [NA]
Justification: [NA]
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent obtained from people whose asset is used.
At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.
14. Crowdsourcing and Research with Human Subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: [NA]
18

---

Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According to the NeurIPS Code Ethics, workers involved in data collection, curation, other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional Review Board (IRB) Approvals or Equivalent for Research with Human Subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: [NA]
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such the institution conducting the review.
19


Reviews:
[{"review_id": "bgJJHMhDqK", "reviewer": "Reviewer_GyCT", "summary": "The paper presents a framework to speed up the pre-training of Vision Transformers (ViTs) in a self-supervised contrastive learning setup. The proposed method incorporates randomized token dropout and flexible patch scaling. The authors leverage this framework to analyze estimated gradient errors and its downstream performance. Additionally, they propose to determine an optimal dynamic acceleration schedule during training. Experimental findings demonstrate improvements in the convergence rate of the MoCo-v3 model across IN-100 and IN-1k datasets.", "questions": "- Line 210: 'The variance is a function of their computational cost.' Could you provide references or further clarification on how variance is a direct function of computational cost?\n- Line 268 - 'The only modification was the use of a non-symmetric loss.' Is there a specific reason for this, considering that MoCo-v3 uses symmetric loss?\n- Line 271 - 'Non-symmetric version produces more diverse batches.' Could you elaborate on what diverse batches mean and how beneficial they are exactly?\n- See weaknesses.", "limitations": "The limitations are not discussed.", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "- The proposed acceleration framework brings noticeable improvements in the pre-training convergence of MoCo-v3 on IN-100 and IN-1k.\n- The framework incorporates various sequence compression strategies. The authors investigate how these strategies affect gradient estimation errors and analyze their impact on downstream performance.\n- The acceleration framework includes a dynamic scheduler that adapts during training. It is validated across different training budgets and supported by ablation studies that highlight the importance of the individual contribution of token dropout and patch-scaling.", "weaknesses": "- The proposed framework consists of two components: (1) randomized token dropout and (2) flexible patch scaling. Both ideas exist and have generally been studied for efficient pre-training of ViTs. For instance, the idea of dropping tokens in ViTs has been explored in various forms since their introduction. Recent research has focused on more sophisticated and targeted ways of applying token dropout in ViTs [1, 2, 3]. As a result, the main contributions of this work may be a bit limited.\n- In section 4.3 - 'Since there are many more linear operations than quadratic ones, the time complexity of linear operations dominates. Thus, for the sake of simplicity, we consider the time complexity of the ViT architecture to be linear in the sequence length'. This statement seems oversimplified. While it's true that there are more distinct linear operations, this doesn't automatically mean they dominate the time complexity. I am not convinced that having more linear operations would negate the impact of the quadratic operation, even with a sequence length of ~200. This assertion requires further clarification and possibly revision.\n- The concepts of randomized token dropout and patch scaling could potentially benefit ViTs in other self-supervised learning (SSL) approaches such as distillation-based SSL (DINO [4], iBOT [5]). I think there will be more contribution if the authors explored pre-training of ViTs in a broader SSL setting than being restricted to just MoCo-v3. \n- I am skeptical about labeling the described approach as truly dynamic acceleration. Although it employs cost-adjusted MSE to compare strategies efficiently, the acceleration schedule is predetermined based on the analysis of intermediate checkpoints from a pre-trained model (as mentioned in section 5.1 under Dynamic Acceleration). A genuinely dynamic acceleration approach would involve real-time adjustments during training. Therefore, the method could be better described as an optimized static schedule that varies across different training stages rather than a fully adaptive, dynamic approach. \n\n[1] Marin, Dmitrii, et al. \"Token pooling in vision transformers.\" arXiv preprint arXiv:2110.03860 (2021).\n\n[2] Ryoo, Michael S., et al. \"Tokenlearner: What can 8 learned tokens do for images and videos?.\" arXiv preprint arXiv:2106.11297 (2021).\n\n[3] Wang, Yulin, et al. \"Not all images are worth 16x16 words: Dynamic transformers for efficient image recognition.\" Advances in neural information processing systems 34 (2021): 11960-11973.\n\n[4] Caron, Mathilde, et al. \"Emerging properties in self-supervised vision transformers.\" Proceedings of the IEEE/CVF international conference on computer vision. 2021.\n\n[5] Zhou, Jinghao, et al. \"ibot: Image bert pre-training with online tokenizer.\" arXiv preprint arXiv:2111.07832 (2021).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the valuable feedback. We are glad the reviewer found the noticeable improvements in ViT contrastive pre-training convergence valuable. This is indeed the flagship result of the paper, which has not been explored in any other prior work. Given the importance of the topic (contrastive pretraining is a foundational pretraining technique that consumes considerable amounts of computational resources) and the positive results demonstrated in this paper, we hope the reviewer reconsiders its score for this reason alone. We will do our best to address the raised concerns in the main paper, as we outline below. If there are any remaining concerns we can clarify, or provide additional results/analysis, please let us know. We will be responsive during the author-reviewer discussion phase.\n## Limited contribution\nWhile it is true that both token dropout and patch scaling have been used in the literature, these techniques have not been studied for efficient gradient approximation (the main topic of our work). One exception is FLIP [22] where token dropout was used for accelerating vision language pre-training. The reviewer mentions prior work on token pruning and token aggregation techniques [1,2,3], however, these works study inference time acceleration, not acceleration of gradient computation. In our case, while we accelerate training convergence by reducing the compute requirements of each training iteration, we do not change the model architecture or its inference graph in any way. The final pre-trained model is still the original VIT. \n\nMore importantly, the paper’s main contribution is the methodology used to obtain the optimized acceleration schedule. The methodology, based on gradient estimation error analysis, is novel and, as shown in the paper. effective. Furthermore, while we focus on contrastive pretraining, the proposed methodology can be generalized to other training regimes. Thus, the publication of this work has the potential for downstream impact beyond contrastive pre-training of VIT models.\n\n## Approximate linear time complexity of low-resolution VITs\nThis statement has been empirically verified. Below we show the time spent on a forward plus backward step for a VIT using varying sequence lengths. Timings were obtained using an RTX A4500 for a batch size of 16. To ensure that pure GPU computation is measured (without any CPU bottlenecks of data loading), randomly generated sequences were used at each iteration. Time measurements were averaged over 20 independent time measurements. As can be seen, the linear approximation is remarkably accurate until a sequence length of 300. In our observations, the quadratic cost of attention mechanisms only becomes a factor at sequence lengths of 750 and above.\n\n| **#Tokens**| 25| 50     | 100    | 150    | 200    | 300    |\n|-|-|-|-|-|-|-|\n| **Time Spent** | 0.030  | 0.046  | 0.086  | 0.132  | 0.171  | 0.267  |\n| **Linear Trendline ($R^2=0.998$)** | 0.024  | 0.047  | 0.089  | 0.133  | 0.176  | 0.263  |\n\n## Generalization to beyond MoCo\nThis is a great suggestion. We have indeed been working on creating optimized acceleration schedules for algorithms beyond MoCo, using the technique proposed in the paper. We were able to achieve a 2.5x speedup in DINO pre-training and a 3.3x speedup in SimCLR pre-training.\n\n\n| Algorithm | Acceleration | Training Budget (M) | NN (%) | LP (%) | FT (%) |\n|---|----|-------|-----|-----|------|\n| **SimCLR** | ✔    | 922      | 50.70        | 68.43    | 81.55    | \n|           | ✗            | 3075        | 50.22        | 68.33    | 81.39    |\n| **DINO (4 small crops)** | ✔      | 1138   | 66.00        | 77.42    | 82.01    |\n|           | ✗            | 2846    | 67.36        | 77.48    | 81.87    |\n\nwhere NN, LP, and FT refer to the accuracies of near neighbor, linear probing, and fine-tuning, respectively. For SimCLR, we simply replaced the backbone in the original implementation (from a ConvNet to ViT-base). As for DINO, the original method performs contrastive learning on both large and small crops. The small crops have a lower computational burden and thus can have a similar effect of speeding up training. To provide a realistic comparison to DINO, both the baseline (unaccelerated) and our accelerated version still use small crops (4 to be exact) in addition to the 2 large crops, with acceleration only applied to large crops. We will add to the paper the results above as well as an analysis of the impact of varying numbers of small crops in DINO.\n\n## Naming proposed approach as dynamic vs optimized acceleration\nWe agree with the reviewer. “Optimized acceleration schedule” is a more appropriate description of the proposed approach. We will revise the paper accordingly. Thanks for pointing this out.\n\n## Variance as a function of computational cost\nWe clarify what we meant in the next sentence (lines 210-212). In short, assume we can decide how much computational budget to provide to approximate one gradient. A simple strategy to improve this approximation is simply to use bigger batch sizes (ie, averaging the gradient over more samples). While averaging does not change estimation bias, it reduces the estimation variance. That’s why we say that variance is a function of the allocated budget. We will clarify this in the paper.\n## Non-symmetric loss\nGiven constant GPU resources, with a non-symmetric loss we can use twice the batch size. This has two advantages, 1) the number of negatives is doubled (an important parameter in contrastive learning); and 2) sample-wise gradients are obtained from truly independent samples (ie, all gradients are obtained from different images). The second point is what we are referring to when saying that the non-symmetric versions use more diverse batches: ie, instead of using the same image to compute two losses and their respective gradients (which can be correlated), the non-symmetric version simply computes each gradient from completely different images.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "bIZvlm5i6g", "reviewer": "Reviewer_geFN", "summary": "This work focuses on speeding up contrastive learning with vision transformers. Two methods, specifically tailored to ViTs, are investigated for making pretraining more efficient: randomised token dropout and flexible patch scaling. Additionally, the authors analyse the gradient estimation errors from these methods and create an automated strategy for optimal acceleration during pretraining. The resulting approach can achieve similar performances for a fraction of the budget, specifically 1/5 for ImageNet-100 and 1/3 for ImageNet-1k.", "questions": "Are the speedup techniques explored in this paper orthogonal/complementary to e.g. resolution scaling or curriculum learning that have been explored in other works?\n\nCan longer training with token dropout and patch scaling yield better results than the baseline, I.e. using the same budget of 520M on ImagetNet-100?\n\nAre the overheads for automatically scheduling the acceleration accounted for in the budget, or are the optimal decision points for the scheduler computed offline and fixed before training?", "limitations": "There is no section that discusses the limitations of the proposed method explicitly. I would like to see some of the questions I’ve asked answered in such a section, or as part of the conclusion.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "**Originality**\nTknDrop is clearly inspired by common usage in MIM and patch scaling is taken from FlexiViT. These are therefore not original ideas but have not been explored for speeding up contrastive ViTs. The more original aspect of the paper, however, is the automatic scheduling of these techniques through gradient error monitoring.\n\n**Quality**\nThe construction of the final dynamic acceleration method is methodical and thorough. Extensive ablations are performed to find the optimal combination that achieves the most effective acceleration. The experimental section overall is of very high quality.\n\n**Clarity**\nThe paper is very clearly written throughout, and the structure is very natural and easy to follow. The plentiful figures and tables are effectively communicating the right information, though Figure 7 could have a larger font. It is however unclear what Lq=50 means in terms of the token dropout ratios (0, 0.25, 0.5, 0.75, 0.9). Does it mean that 50 tokens are kept out of the total 197 (meaning roughly 0.75 for the dropout ratio)?\n\n**Significance**\nThe simplicity and automated nature of the method makes it seem easy for practitioners and researchers to use themselves. This can indeed facilitate a wider adoption of self-supervised pretraining for those with modest compute resources. However, while the authors point out that their narrow focus on MoCov3 + contrastive learning + ViT allows for a deeper study, the paper suffers a bit by not showing any generalisation to other types of training, like supervised pre-training on ImageNet.", "weaknesses": "My main concern is that the scope of the paper is rather narrow. It does one thing and it does it well, but this limits its potential impact. The proposed method is not limited to only contrastive pretraining and a single experiment that shows how it also applies to supervised pretraining on ImageNet would help show how generally applicable it is.\n\nIt’s not quite clear how the values 1/3 and 1/5 for the budget are obtained for the caption of Figure 1. Section 6.4 claims a 4x speedup on ImageNet. These claims can be made more consistent throughout.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the valuable feedback. We are glad to see the originality of the proposed method and the significance and quality of our empirical results appreciated. We will revise the paper to add experiments and clarify unclear points, as described below. If there are any remaining concerns we can clarify, or provide additional results/analysis, please let us know. We will be responsive during the author-reviewer discussion phase.\n## Generalization to other pretraining frameworks\nWhile we plan to investigate other pretraining frameworks such as supervised learning, vision-language pretraining, and even object detection/segmentation in a future journal extension of the paper, we focused on contrastive pre-training since contrastive learning is famously slow to converge, often requiring up to 1000 epochs to obtain optimal performance. Having said that, we have indeed been working on creating optimized acceleration schedules for algorithms beyond MoCo, using the technique proposed in the paper. We were able to achieve a 2.5x speedup in DINO pre-training and a 3.3x speedup in SimCLR pre-training.\n\n| Algorithm | Acceleration | Training Budget (M) | NN (%) | LP (%) | FT (%) |\n|-----------|--------------|---------------------|--------------|----------|----------|\n| **SimCLR** | ✔            | 922                 | 50.70        | 68.43    | 81.55    | \n|           | ✗            | 3075                | 50.22        | 68.33    | 81.39    |\n| **DINO (4 small crops)** | ✔            | 1138                | 66.00        | 77.42    | 82.01    |\n|           | ✗            | 2846                | 67.36        | 77.48    | 81.87    |\n\nwhere NN, LP, and FT refer to the accuracies of near neighbor, linear probing, and fine-tuning, respectively. For SimCLR, we simply replaced the backbone in the original implementation (from a ConvNet to ViT-base). As for DINO, the original method performs contrastive learning on both large and small crops. The small crops have a lower computational burden and thus can have a similar effect of speeding up training. To provide a realistic comparison to DINO, both the baseline (unaccelerated) and our accelerated version still use small crops (4 to be exact) in addition to the 2 large crops, with acceleration only applied to large crops. We will add to the paper the results above as well as an analysis of the impact of varying numbers of small crops in DINO.\n\n\n## Relation to other speedup techniques\nThe main difference between the prior techniques mentioned in the paper and our work is that prior works are not tailored to VITs. Curriculum learning strategies can be applied to any model and used in conjunction with our VIT-specific techniques for potentially larger speedups. \nResolution scaling explores a similar idea to dynamic patch scaling. The main difference is that resolution scaling simply resizes the input images, while patch scaling adjusts the patch projection layer instead, in a more principled fashion (as extensively discussed in FlexiVIT). Prior work has explored resolution scaling mostly with CNNs, but it could also be extended to VITs. Given the similarities, combining resolution and patch scaling should not lead to major improvements. \n## Longer training schedule\nIn the algorithms that we tested, longer training schedules did not lead to improved performance. This is likely because we have chosen algorithms that have been fully optimized until convergence. As can be seen in Fig 7 of the main paper, when acceleration is used for too long, the model can overfit (NN accuracy of the model trained with a constant 75% dropout ratio drops in the 2nd half of training). We found that using long schedules will often lead to the overuse of acceleration and consequently performance drops. These drops can be recovered later on, after the schedule prescribes less acceleration, but still, equal or lower performance was observed at the end of training.\n## How is the scheduler computed?\nThe schedule is computed offline and fixed before training. The same optimized schedule is then used across many runs in the paper (eg, with different total budgets). Since the schedule is fixed, it has no additional overhead during training.\n## Discussion of limitations\nThank you for pointing this out. We will add a discussion of limitations/future work to the conclusion. In particular, we will highlight the potential of the method being used for other pre-training frameworks, as well as, the potential of the proposed method to be deployed in an online fashion for truly dynamic acceleration schedules.\n## What Lq=50 means?\nIt means that a total sequence length of 50 tokens (out of the initial 196) is fed to the query encoder.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PfHFb4K1K2", "reviewer": "Reviewer_Q1ps", "summary": "This paper presents an acceleration framework for Vision Transformers in contrastive learning. It utilizerandomized token dropout and patch-scaling to reduce the sequence length to accelerate training. Based on an analysis of the gradient estimation error, this paper proposes an automated procedure to identify an optimal acceleration schedule. Extensive experiments demonstrate that accelerated pretraining achieves comparable performance on visual understanding tasks, while effectively reducing computation costs.", "questions": "Please see the comments above.", "limitations": "Please see the comments above.", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "Strength：\n\n1. The motivation is clear and reasonable.\n2. Extensive experiments demonstrate the approach's effectiveness in reducing the training time while keeping comparable comparisons. \n3. This paper is well-organized and clearly-written.", "weaknesses": "Weakness:\n\n1. Lower ceiling. It seems like that this framework will lead to a decrease in the optimal performance of pretraining. For  instance, in Fig.1(b), Accelerated MoCo's highest performance is lower than MoCo, although much faster. By the way, ImageNet-1K performance is much more important than ImageNet-100, especially for a SSL method.\n2. Missing important metric. This paper reports NN and Linear Probing to evaluate the approach, however, full-finetuning top-1 accuracy (on ImageNet-1K) is a critical metric for SSL method. Adding this metric can make this paper more convincing.\n3. Changed structure. Since this paper utilizes flexible patch embedding, the structure is not a naive ViT anymore, which may undermine the application of this paper in many scenarios.\n4. Limited evaluation. This paper only conduct experiments based on MoCo-v3, more evaluation on other SSL frameworks (e.g, another contrastive learning method or a MIM method) will make it solid.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate the valuable feedback. Below we answer the main concerns, and we will revise the paper accordingly. If there are any remaining concerns we can clarify or provide additional results/analysis, please let us know. We will be responsive during the author-reviewer discussion phase. Given the added experiments and evaluations, as well as the importance of accelerating contrastive pretraining, we hope the reviewer will reconsider the recommendation score. \n## Lower ceiling\nFirst, we note that evaluating SSL methods is not a trivial task, and different researchers use/advocate for different evaluation metrics. Having said that, the most commonly used metric for assessing representation learning of SSL methods is Linear Probing accuracy on ImageNet-1k. Under this metric, the acceleration procedure achieves the **same** performance (only 0.1% lower) to the baseline MoCo. We understand that the NN accuracy is slightly worse (1.2% lower). However, this metric is known to be more sensitive to slight changes in the model since no weights are trained to adapt the model output to the downstream task. While we find it informative (the reason why we added it to the paper), linear probing is a more important metric. Also, while we understand that ImageNet-1K results are more important than ImageNet-100, the significantly higher NN accuracy in ImageNet-100 is still worth pointing out.\n## Full finetuning\nThanks for the suggestion. We plan to add full finetuning results to the paper. Unfortunately, some of the checkpoints we had trained were lost. We are in the process of retraining and will provide the results, hopefully, during the author-reviewer discussion phase.\n## Changed VIT architecture\nWe believe there was a misunderstanding. Patch scaling is only used for accelerating training. At inference time, the learned VIT simply reverts to the original patches of size 16. No changes were made to the model’s inference computational graph.\n## Evaluation beyond MoCo\nThis is a great suggestion. We have indeed been working on creating optimized acceleration schedules for algorithms beyond MoCo, using the technique proposed in the paper. We were able to achieve a 2.5x speedup in DINO pre-training and a 3.3x speedup in SimCLR pre-training.\n\n\n| Algorithm | Acceleration | Training Budget (M) | NN (%) | LP (%) | FT (%) |\n|-----------|--------------|---------------------|--------------|----------|----------|\n| **SimCLR** | ✔            | 922                 | 50.70        | 68.43    | 81.55    | \n|           | ✗            | 3075                | 50.22        | 68.33    | 81.39    |\n| **DINO (4 small crops)** | ✔            | 1138                | 66.00        | 77.42    | 82.01    |\n|           | ✗            | 2846                | 67.36        | 77.48    | 81.87    |\n\nwhere NN, LP, and FT refer to the accuracies of near neighbor, linear probing, and fine-tuning, respectively. For SimCLR, we simply replaced the backbone in the original implementation (from a ConvNet to ViT-base). As for DINO, the original method performs contrastive learning on both large and small crops. The small crops have a lower computational burden and thus can have a similar effect of speeding up training. To provide a realistic comparison to DINO, both the baseline (unaccelerated) and our accelerated version still use small crops (4 to be exact) in addition to the 2 large crops, with acceleration only applied to large crops. We will add to the paper the results above as well as an analysis of the impact of varying numbers of small crops in DINO", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "G5X1pUDWR6", "reviewer": "Reviewer_95eS", "summary": "This submission proposed to accelerate the training of ViT by two methods: 1) Randomly drop tokens for input. 2) Dynamically resize patches into different dimension. The second method is published in previous works.", "questions": "1. In Sec. 4.2, author mentioned that \"large patches cannot be directly encoded\", why large patches cannot be encoded (to what and by what?) Please add more explanation on the motivation of patch scaling.", "limitations": "N.A.", "rating": 3, "confidence": 2, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "N.A.", "weaknesses": "1. The novelity of the sumission is limited: In first propsoed method, dropping tokens randomly (masking) is a common trick for performance improvement (as cited by authors). It is natural to connect improvement to training acceleration. For the second method, as author point out that the method comes from published work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We are sad that the reviewer completely misunderstood the contributions and the significance of the proposed work. We made clear both in the related work and method sections that Token Dropout and Patch Scaling are NOT contributions of our work, by explicitly citing the origins of each technique. The contribution of our work is how to design an optimized acceleration schedule, based on gradient estimation error analysis, so as to optimally define how much compression to use at different phases of training, and how to optimally combine these previously proposed techniques to achieve the required compression rate. We urge the reviewer to read the paper again and carefully reassess it based on our actual contributions.  We will be responsive during the author-reviewer discussion phase, in case there are any concerns we can address.\n## Why large patches cannot be encoded (to what and by what?)\nThe first step in a VIT is to embed each $p \\times p$ patch into a fixed-size embedding vector (of size $n$). This is done by a linear projection matrix of size, $p^2 \\times n$ (or equivalently implemented as a conv layer of kernel size $p$). Since the projection matrix is fixed size, it can’t be applied to larger (or smaller) patches.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This work explores two approaches to accelerate contrastive learning with vision transformers (ViTs) and improve pretraining efficiency. The authors also analyze gradient estimation errors related to these methods and propose an automated strategy for optimal acceleration during pretraining. The approach achieves comparable performance. In the rebuttal, the authors provided additional experiments addressing the reviewers' feedback. We encourage the authors to fully incorporate the reviewers' comments, clarify the major contributions of the proposed solution, and resolve any inconsistencies, such as whether Dynamic Acceleration is applied on-the-fly or as post-processing.
Decision: Accept (poster)