Proper Straight-Through Estimator: Breaking symmetry promotes convergence to true minimum

In the quantized network , its gradient shows vanishing except for nondifferentiable points . The network thus can not be learned by the standard backpropagation , so that an alternative approach called Straight Through Estimator ( STE ) , which replaces the part of the gradient with a simple differentiable function , is used . While STE is known to work well for learning the quantized network empirically , it has not been established theoretically . A recent study by Yin et al . ( 2019 ) has provided theoretical support for STE . However , its justification is still limited to the model in the one-hidden layer network with the binary activation where Gaussian generates the input data , and the true labels are output from the teacher network with the same binary network architecture . In this paper , we discuss the effectiveness of STEs in more general situations without assuming the shape of the input distribution and the labels . By considering the scale symmetry of the network and specific properties of the STEs , we find that STE with clipped Relu is superior to STEs with identity function and vanilla Relu . The clipped Relu STE , which breaks the scale symmetry , may pick up one of the local minima degenerated in scales , while the identity STE and vanilla Relu STE , which keep the scale symmetry , may not pick it up . To confirm this observation , we further present an analysis of a simple misspecified model as an example . We find that all the stationary points are identical with the vanishing points of the cRelu STE gradient , while some of them are not identical with the vanishing points of the identity and Relu STE . Finally we have numerically confirmed the observation for the mixture Gaussian model with various teacher network . 1 INTRODUCTION Quantization of the weights and the activations is a promising technique to save the memory and accelerate the inference speed in deep neural networks ( DNNs ) which have been getting wider and deeper in recent years . There are two main approaches of the quantization for DNNs ( Krishnamoorthi ( 2018 ) ) : Post-training quantization ( PTQ ) and quantization-aware training ( QAT ) . In the PTQ , the pre-trained networks are simply quantized without re-training the model . This approach allows us to achieve the nearly floating point accuracy at 8-bits , while below 8-bits , this results in significant accuracy degradation . Although there have been recent attempts to alleviate the accuracy degradation in PTQ ( Banner et al . ( 2018 ) ; Choukroun et al . ( 2019 ) ; Zhao et al . ( 2019 ) ; Kryzhanovskiy et al . ( 2021 ) ) , the QAT , where the quantized weights and activations are trained ( e.g. , Zhou et al . ( 2016 ) ; Hubara et al . ( 2017 ) ; Rastegari et al . ( 2016 ) ) , usually leads to better accuracy . The difficulty in QAT is that the weights and the activations are discretized , and intrinsically nondifferentiable . If we take the derivatives forcibly , they either vanish or diverge . To avoid this problem , we replace them with the derivatives of some differentiable function in the backward pass only , called the Straight-Through Estimator ( STE ) ( Hinton et al . ( 2012 ) ; Bengio et al . ( 2013 ) ) . Since the replacement leads to bias , it is not always possible to learn the network successfully . However this approach can be applied to the low bits below 8-bits with tolerable accuracy degradation ( Zhou et al . ( 2016 ) ; Choi et al . ( 2018 ) ; Esser et al . ( 2019 ) ; Bhalgat et al . ( 2020 ) ) . Originally , STE was introduced as the replacement with the derivative of the identity function by Hinton et al . ( 2012 ) . Later , the term ” STE ” has been extensively used as the replacement with various functions . In the binary network , Bengio et al . ( 2013 ) studied the replacement with the derivative of sigmoid function as well as the original STE , and Hubara et al . ( 2017 ) used the derivative of the identity function clipped in the region of |x| ≤ 1 . In low-bits network , Zhou et al . ( 2016 ) used the quantized gradient after the replacement with the identity function , and Choi et al . ( 2018 ) , Esser et al . ( 2019 ) , and Bhalgat et al . ( 2020 ) used the the derivative of the clipped Relu ( cRelu ) for leaning the step size . 1.1 MAIN CONTRIBUTION In this paper , we refer to the proxy of the gradient as the STE gradient 1 . Although STE has been much success in training quantized DNNs empirically , the theoretical justification is very limited to the specific situations . It also remains unclear what differentiable functions should be used for the STE gradient . Our aim is to shed light on a new factor to determine the functions . Without assuming the shape of the input distribution and the loss function , we first discuss the properties of the three STEs , identity STE , Relu STE , and cRelu STE in one-hidden-layer network with binary activation from the perspective of the intrinsic symmetry . We find that because the identity STE and Relu STE keep the scale symmetry , the STE gradients should be zero for any scale to converge to the true ( local ) minimum of the loss function , while in the case of the cRelu STE , which breaks the scale symmetry , even if its gradient is not zero at the minimum at some scale and it becomes zero at other scale , the cRelu STE converges to the minimum . Therefore , the backpropagation using the cRelu STE is most likely to converge to the minimum of the loss function among three STEs . This result reveals differences between Relu STE and cRelu STE , which could not be found in the models by Yin et al . ( 2019 ) and Long et al . ( 2021 ) . Recently , Kunin et al . ( 2020 ) discussed the symmetry embedded in non-quantized neural networks , and the effect of its breaking such as the discretization , the weight decay , and the stochasticity during training . We discuss the new effect of breaking symmetry by the proxy of the gradient in training the quantized neural networks . Next , to confirm this observation , we have studied a similar model of the Gaussian input as discussed in Yin et al . ( 2019 ) as an example . We have employed a misspecified model suitable for most practical situations . Unlike the previous study , the true labels are presumed to be generated by the non-quantized network architecture . Consequently , we find that ignoring the scale degeneracy for the weight in front of the activation , all the stationary points are identical with the vanishing points in the cRelu STE gradient , while some of them are not identical with the vanishing points in the identity and the Relu STE gradients . In particular , at the global minimum point , both identity and Relu STE gradients do not vanish . Finally , to confirm it in more general case , we have numerically studied the model of the mixture Gaussian input . 1.2 RELATED WORKS Recently , there have been a few theoretical studies on the justification of the STEs . Shekhovtsov & Yanush ( 2020 ) derived STE by the linearization of their proposed estimator in a stochastic binary deep network where the noises are injected before the binary activation to be a smooth network . Cheng et al . ( 2019 ) argued that STE can be interpreted as a projected Wasserstein gradient flow under certain conditions . Yin et al . ( 2019 ) that inspired our study examined which of the three STE ’ s , identity STE , Relu STE , and cRelu STE , converges to the true minimum in one-hiddenlayer network with binarized activation and Gaussian input data . They clarified that if the labels are obtained from a teacher network of the same architecture ( Du et al . ( 2018 ) ) , all the three STEs show the non-negative correlation with the population gradient , but only the identity STE gradient does not become zero in a local minimum . This indicates that the back-propagation using either Relu STE or cRelu STE may convergence to the local minimum , while it is impossible to show the convergence to the local minimum using the identity STE . Furthermore , they showed that all the three STEs gradients vanish at the global minimum , indicating that they may achieve the global minimum independent of the choice of the STE , if we start with an appropriate initial value . 1It is called “ coarse ” gradient in Yin et al . ( 2019 ) . Very recently , Long et al . ( 2021 ) have discussed the justification of a class of STEs with certain monotonicity , which generalizes the Relu STE , in one-hidden-layer network for the hinge loss , claiming that the STE gradients vanish at the global minimum . 1.3 NOTATIONS |·| denotes the Euclidean norm of a vector . A capital letter in bold denotes the matrix , e.g. , Z , and its component is given by the letter with lower indices , e.g. , Zij . A small letter in bold denotes the vector , e.g. , g , and its component is given by the letter with lower indices , e.g. , gj . Im×m is the m×m identity matrix , and 1m is the m-dimensional vector of all ones . 2 GENERAL DISCUSSION ON STES IN ONE-HIDDEN LAYER CNN WITH BINARY ACTIVATION 2.1 PRELIMINARIES The one-hidden layer convolutional neural network with a binary activation function is realized as y ( Z , v , w ) = vTσ ( Zw ) = ∑ i viσ ( ∑ j Zijwj ) , ( 1 ) where σ ( · ) is the step function , σ ( x ) = { 1 x > 0 , 0 x ≤ 0 , ( 2 ) Z ∈ Rm×n is constructed by deividing the input data , e.g . an image , into m patches with size n , and w ∈ Rn and v ∈ Rm are the trainable weights , i.e . kernels , in the first and second layers , respectively ( Du et al . ( 2018 ) ) . We assume that Z is generated by a continuous distribution P ( Z ) . Due to the property of the step function , the network is invariant under the scale transformation for w , w → kw ( k > 0 ) . Using the loss for each sample , ` ( y ( v , w , Z ) ; y∗ ) with y∗ being the labels generated by the true distribution q ( y∗|Z ) , we can express the population loss function as L ( v , w ; y∗ ) = EZ [ ` ( y ( v , w , Z ) ; y∗ ) ] . ( 3 ) 2.2 BACK-PROPAGATION AND STE GRADIENTS The gradients of the loss for each sample w.r.t v and w can be formally expressed as ∂ ` ∂vi = ∂ ` ∂y ∂y ∂vi = ∂ ` ∂y σ ( ∑ j Zijwj ) , ( 4 ) ∂ ` ∂wj = ∑ i ∂ ` ∂xi ∂xi ∂wj = ∑ i ∂ ` ∂y viδ ∑ j′ Zij′wj′ Zij , ( 5 ) where xi = σ ( ∑ j Zijwj ) and we used the property that the derivative of the step function is Dirac ’ s delta function , dσ ( z ) /dz = δ ( z ) . The gradient w.r.t v can be utilized for learning the network , while it is impossible to use the gradient w.r.t w due to the presence of the delta function . Remarkably , both population gradients w.r.t v and w are smooth functions due to its average over the continuous distribution , EZ [ ∂ ` ∂vi ] = ∫ ∏ i′ , j′ dZ̃i′j′ P ( { Z̃i′j′ } ) ∂ ` ∂y σ ( Z̃i1|w| ) , ( 6 ) EZ [ ∂ ` ∂wj ] = 1 |w| ∑ i Fij ( v , w , Z̃i1 = 0 ) , Fij ( v , w , Z̃i1 ) ≡ ∫ ∏ ( i′ , j′ ) 6= ( i,1 ) dZ̃i′j′ P ( { Z̃i′j′ } ) [ ∂ ` ∂y vi ∑ k′ Oik′jZ̃ik′ ] , ( 7 ) where we used the orthogonal transformation , Z̃ik′ = ∑ j′ Oik′j′Zij′ , Zij′ = ∑ k′ Oik′j′Z̃ik′ , with Oi1j′ = wj′/|w| and ∑ j′ Oik′j′Oil′j′ = δk′ , l′ . We can easily see that the gradient w.r.t . w does not have the component in w-direction , ∑ j wjEZ [ ∂ ` ∂wj ] = 0 . Its vanishing originally follows from the scale symmetry of the network : Because the loss has the scale symmetry , its gradient in the w-direction is obviously zero . Instead of the population gradient , we use the STE in practice , which replaces the delta function with the derivative of some differentiable function µSTE , to train the kernel w , gSTEj ( v , w , Z ) ≡ ∑ i ∂ ` ∂y viµ ′ STE ∑ j′ Zij′wj′ Zij . ( 8 ) We consider three types of µSTE ( x ) : ( i ) the identity type µSTE ( x ) = x , ( ii ) vanilla ReLU type µSTE ( x ) = xσ ( x ) and ( iii ) the cReLU type µSTE ( x ) = xσ ( x ) σ ( r − x ) with the upper clipping value r. The surrogate back-propagation using the STE is described in Algorithm 1 ( Yin et al . ( 2019 ) ) . Algorithm 1 Surrogate back-propagation using STE for learning one-hidden-layer CNN . Input : initialization v0 ∈ Rm , w0 ∈ Rn , learning rate η. for t = 0 , 1 , , . . . do vt+1 = vt − η EZ [ ∂ ` ∂v ( v t , wt ; Z ) ] wt+1 = wt − ηEZ [ gSTE ( vt , wt ; Z ) ] end for The average of the STE gradients over the input data is also expressed as EZ [ gSTEj ( v , w ; Z ) ] = ∑ i ∫ dZ̃i1µ ′ STE ( Z̃i1|w| ) Fij ( v , w , Z̃i1 ) . ( 9 ) Note that in general , the STE gradients have the non-zero component in the w-direction due to their partial collapse of the form of the gradient , ∑ j wjEZ [ gSTEj ( v , w ; Z ) ] 6= 0 . It is obvious from Eq . ( 9 ) that the only difference between the STE gradients with the identity function , Relu , and cRelu is the region of the integration over Z̃i1 , EZ [ gAj ( v , w ; Z ) ] = ∑ i ∫ RA dZ̃i1Fij ( v , w , Z̃i1 ) , ( 10 ) where A = id , Relu , cRelu , and the integration reigion RA are given as Rid = ( −∞ , +∞ ) , RRelu = [ 0 , +∞ ) , andRcRelu = [ 0 , r|w| ] . Following the scale invariance of Fij ( v , w , Z̃i1 ) for w , Fi′j ( v , kw , Z̃i′1 ) = Fi′j ( v , w , Z̃i′1 ) ( k > 0 ) , the STE gradients have the following interesting features : 1 . The identity and Relu STE gradients have scale invariance for w , EZ [ g id/Relu j ( v , kw ; Z ) ] = EZ [ g id/Relu j ( v , w ; Z ) ] . ( 11 ) 2 . The cRelu STE gradient does not have scale invariance for w due to the clipping effect , EZ [ gcReluj ( v , kw , r ; Z ) ] 6= EZ [ gcReluj ( v , w , r ; Z ) ] , ( 12 ) where we have explicitly shown the dependence of the upper clipping value r. 3 . Instead , the scale transformation of w for the cRelu STE gradient can be compensated by that of r : EZ [ gcReluj ( v , w , kr ; Z ) ] = EZ [ gcReluj ( v , w/k , r ; Z ) ] . ( 13 ) If we take kr with fixed r large enough to cover most of the distribution P ( Z ) , the left-hand side is identical with the Relu STE gradient . Here we define the spread of the distribution as ρ . When we redefine w/k as w on the right hand , the cRelu STE gradient for |w| < r/ρ is approximately identical with the Relu STE gradient , EZ [ gcReluj ( v , w , r ; Z ) ] ' EZ [ gReluj ( v , w ; Z ) ] for |w| < r/ρ . ( 14 ) This result is intuitively obvious : when w is small enough to keep most of the pre-activation values in the clipped range , the cRelu gradient and the Relu gradient are approximately identical . The important issue is that there is no guarantee that the weights obtained by the STE backpropagation are the ones at the ( local ) minimum of the loss function . In other words , at the stationary points , ( v , w ) = ( vs , ws ) , defined as the vanishing points of the population gradient , EZ [ ∂ ` ∂vi ( v = vs , w = ws ) ] = 0 , EZ [ ∂ ` ∂wj ( v = vs , w = ws ) ] = 0 , ( 15 ) the STE gradient is not zero in general . Here we consider the local minimum point that the STE gradient does not vanish , EZ [ gSTEj ( vs , ws ; Z ) ] 6= 0 . ( 16 ) Note that ( v , w ) = ( vs , kws ) for any k > 0 is also the local minimum point due to the scale invariance of the loss . In the case of the Relu/identity STEs , their gradient has the same finite value for any scales due to the scale invariance shown in Eq . ( 11 ) , EZ [ g id/Relu j ( vs , kws ; Z ) ] = EZ [ g id/Relu j ( vs , ws ; Z ) ] 6= 0 . ( 17 ) In the case of the cRelu STE , however , the value of its gradient changes as the scale changes due to the breaking of the scale symmetry shown in Eq. ( 12 ) . If it crosses zero as a function of scale , EZ [ gcReluj ( vs , k0ws , r ; Z ) ] = 0 for some k0 , ( 18 ) the back propagation using cRelu STE can converge at the local minimum as was done by using the population gradient . Therefore , we conjecture that due to breaking scale symmetry , the backpropagation using cRelu STE is the most likely to achieve the ( local ) minimum of the loss function in the three STEs . This also implies that the cRelu STE is less biased than the other . We confirm the conjecture analytically for the Gaussian input with Relu-type teacher network in Sec.3 , and numerically for the mixture Gaussian input with various teacher network in Sec . 4 . Since the above discussion is focused only on symmetry , the conjecture can be generalized to networks with more symmetries in a straightforward way . In that case , if we employ an STE that breaks all of those symmetries instead of cRelu STE , it is more likely to achieve the minimum . The similar mechanism can be also found in physical phenomena . For instance , this is the case of the ferromagnet , which has rotational symmetry in energy ( or Hamiltonian ) . In the system , if we impose the magnetic field , which breaks the rotational symmetry , the spin states , corresponding to minimum points , become aligned with the same direction . 3 ONE-HIDDEN LAYER CNN WITH BINARY ACTIVATION : GAUSSIAN INPUT AND LABELS GENERATED BY NON-QUANTIZED RELU NETWORK We consider the simple model similar to Yin et al . ( 2019 ) given by the Gaussian input with the variance σ̂2 , Z ∼ P ( Z ) = ( 1√ 2πσ̂2 ) mn e− ∑ i , j Z2ij 2σ̂2 . We use the squared loss function , ` ( y ( v , w , Z ) ; y∗ ( v∗ , w∗ , Z ) ) = 12 ( y − y ∗ ) 2 . Unlike in Yin et al . ( 2019 ) , the true labels are assumed to be generated by the non-quantized Relu network , y∗ ( v∗ , w∗ , Z ) = ∑ i v∗i fRelu ∑ j Zijw ∗ j , ( 19 ) with the Relu activation defined as fRelu ( x ) = xσ ( x ) . Note that the models are misspecified , i.e. , the population loss cannnot be zero even at the global minimum . In fact , it is inferred from the accuracy degradation in most practical situations ( Hubara et al . ( 2017 ) ) that the binary networks are considered as misspecified models . On the other hand , if the true labels are generated by the same binarized architecture as was used in Yin et al . ( 2019 ) , the network becomes a specified model . As shown below , this misspecified model leads to a more striking difference between three STEs than the specified model . Particularly , the behaviors of three STE gradients at the global minimum are completely different from each other . 3.1 POPULATION LOSS , ITS GRADIENT AND STATIONARY POINTS The population loss L ( v , w ; v∗ , w∗ ) is derived in Appendix A.1 as L ( v , w ; v∗ , w∗ ) = EZ [ ` ( v , w , Z ; v∗ , w∗ ) ] = 1 8 vT ( Im×m + 1m1 T m ) v − σ̂|w ∗| 2 √ 2π vT ( cosϕIm×m + 1m1 T m ) v∗ + σ̂2 4π |w∗|2v∗T ( ( π − 1 ) Im×m + 1m1Tm ) v∗ ( 20 ) where ϕ is the angle between w∗ and w. Note that the loss is scale invariant for w , and thus its derivative satisfies ew · ∂L∂w = 0 . The stationary points are given by ∂L ∂v = 0⇔ v = 2σ̂|w ∗|√ 2π ( cosϕIm×m + 1− cosϕ m+ 1 1m1 T m ) v∗ ( 21 ) ∂L ∂w = 0⇔ vTv∗ = 0 or ϕ = 0 or ϕ = π ( 22 ) where we used ( Im×m + 1m1 T m ) −1 = Im×m − 1m+11m1 T m. There are three stationary points 2 as classified in Appendix A.2 : 1. middle saddle point given by v = v̄ ≡ 2σ̂|w ∗|√ 2π ( cos ϕ̄Im×m + 1−cos ϕ̄ m+1 1m1 T m ) v∗ , ϕ = ϕ̄ ≡ arccos [ ( v∗T 1m ) 2 − ( m+1 ) ( v∗ ) 2+ ( v∗T 1m ) 2 ] , which satisfies v̄Tv∗ = 0 . This exists if and only if ( m+ 1 ) ( v∗ ) 2 ≥ 2 ( v∗T1m ) 2 . 2. local minimum if ( m + 1 ) ( v∗ ) 2 ≥ 2 ( v∗T1m ) 2 , otherwise saddle point given by v = vπ ≡ 2σ̂|w ∗|√ 2π ( −Im×m + 2m+11m1 T m ) v∗ , ϕ = π . 3. global minimum given by v = v0 ≡ 2σ̂|w ∗|√ 2π v∗ , ϕ = 0 . At the global minimum , the population loss does not become zero , L ( v = v0 , w = w∗ ) = σ̂2|w∗|2 ( π−2 ) 4π v ∗Tv . 3.2 STE GRADIENT The STE gradient is given by gSTEj = ∑ i viµ ′ STE ∑ j′ Zij′wj′ Zij × ∑ i′ vi′σ ∑ j′ Zi′j′wj′ −∑ i′ v∗i′fRelu ∑ j′ Zi′j′w ∗ j′ . ( 23 ) 2The definition of the stationary points is not mathematically rigorous in our paper . Even if points are non-differentiable , but the gradient gives zero at the points in the one-side limit , we call them the stationary points . The converging points by the back-propagation in Algorithm 1 are characterized by vanishing the gradients , EZ [ ∂ ` ∂vi ( v , w ; Z ) ] = 0 , EZ [ gSTEj ( v , w ; Z ) ] = 0 . ( 24 ) We discuss the explicit form of the three STE gradients and the associated vanishing points . The detailed derivation of the STE gradients is shown in Appendix A.3-A.5 . 3.2.1 IDENTITY STE The identity STE gradient is given by EZ [ gid ] = σ̂√ 2π w |w| ( vTv ) − σ̂ 2 2 w∗ ( vTv∗ ) ( 25 ) Combined with Eq . ( 21 ) , we find that the equation in Eq . ( 24 ) has no solution . Therefore , if we use the back-propagation by identity STE , it can not converge to any points . 3.2.2 RELU STE The Relu STE gradient is expressed as EZ [ grelu ] = σ̂ 2 √ 2π w |w| [ vT ( Im×m + 1m1 T m ) v ] − σ̂ 2 2π |w∗| |w| w [ vT ( −Im×m + 1m1Tm ) v∗ ] − σ̂2 ( ( π − ϕ ) w∗ + |w ∗| sinϕ |w| w ) ( vTv∗ ) 2π . ( 26 ) Combined with Eq . ( 21 ) , the gradients vanish at the following points : 1 . If v∗ satisfies ( m+ 1 ) ( v∗ ) 2 ≥ 2 ( v∗T1m ) 2 , ϕ = ϕ̄ = arccos [ ( v∗T 1m ) 2 − ( m+1 ) ( v∗ ) 2+ ( v∗T 1m ) 2 ] , v = v̄ = 2σ̂|w ∗|√ 2π ( cosϕIm×m + 1−cosϕ m+1 1m1 T m ) v∗ . 2. ϕ = π , v = v̄ = 2σ̂|w ∗|√ 2π ( −Im×m + 2m+11m1 T m ) . We find the solutions are identical with two stationary points shown in item 1 and 2 in Sec.3.1 , leading to the the saddle point or local minimum , while the global minimum point at ϕ = 0 , v = v0 can not be obtained . Therefore , by using the back-propagation with Relu STE , it always converges to ϕ = π if ( m+ 1 ) ( v∗ ) 2 ≥ 2 ( v∗T1m ) 2 , and it does not converge to any points , otherwise . 3.2.3 CLIPPED RELU STE The cRelu STE is given by EZ [ gcrelu ] = σ̂ 2 √ 2π w |w| ( 1− e− 1 2 ( r σ̂|w| ) 2 ) vT ( Im×m + 1m1 T m ) v − σ̂ 2 2π |w∗| |w| ( 1− e− 1 2 ( r σ̂|w| ) 2 ) w [ vT ( −Im×m + 1m1Tm ) v∗ ] − σ̂2 { C ( w , ϕ ) w∗ |w∗| + S ( w , ϕ ) ( 1 sinϕ w |w| − cotϕ w ∗ |w∗| ) } ( vTv∗ ) , ( 27 ) where C ( |w| , ϕ ) and S ( |w| , ϕ ) are given in Eq. ( 106 ) . At ϕ = 0 , π they are simplified as C ( |w| , 0 ) = |w ∗| 2π ( π − rσ̂|w|e − 12 ( r σ̂|w| ) 2√ 2π − πerfc ( r√ 2σ̂|w| ) ) , C ( |w| , π ) = S ( |w| , π ) = S ( |w| , 0 ) = 0 . Remarkably , the cRelu STE gradient is proportional to the Relu STE gradient in the case of vTv∗ = 0 or ϕ = π : EZ [ gcrelu ] |vT v∗=0 or ϕ=π = ( 1− e− 1 2 ( r σ̂|w| ) 2 ) EZ [ grelu ] |vT v∗=0 or ϕ=π . ( 28 ) As shown in Sec.3.2.2 , all the vanishing points in the Relu STE can be found at vTv∗ = 0 or ϕ = π , leading to the saddle point or the local minimum , so that they are also found in the vanishing points of cRelu STE . On the other hand , the behavior of cRelu STE gradient at ϕ = 0 is not related to that of Relu STE gradient . The cRelu STE gradient at ϕ = 0 is written as EZ [ gcrelu ] = σ̂ 2 √ 2π w∗ ( 1− e− 1 2 ( r σ|w| ) 2 ) vT { 1 |w∗| ( Im×m + 1m1 T m ) v − 2σ̂√ 2π ( ( −1 + λ ) Im×m + 1m1Tm ) v∗ } , ( 29 ) where λ = λ ( |w| ) = π − rσ̂|w|e − 12 ( r σ̂|w| ) 2√ 2π − πerfc ( r√ 2σ̂|w| ) 1− e− 1 2 ( r σ̂|w| ) 2 . ( 30 ) This becomes zero if v = 2σ̂√ 2π |w∗| ( Im×m + 1m1 T m ) −1 ( ( −1 + λ ) Im×m + 1m1Tm ) v∗ = 2σ̂√ 2π |w∗| ( ( −1 + λ ) Im×m + 2− λ m+ 1 1m1 T m ) v∗ . ( 31 ) To be consistent with Eq . ( 21 ) at ϕ = 0 , only λ = 2 is allowed . The solution provides the global minimum shown in item 3 in Sec.3.1 . In fact , λ monotonically increases from 0 to π as rσ̂|w| increases , and thus the solution at λ = 2 , corresponding to the global minimum point , can be obtained for any clipping value r and the variance σ̂2 by changing the scale |w| . Consequently , we get all the stationary points are found in cRelu STE : 1 . If v∗ satisfies ( m+ 1 ) ( v∗ ) 2 ≥ 2 ( v∗T1m ) 2 , ϕ = ϕ̄ = arccos [ ( v∗T 1m ) 2 − ( m+1 ) ( v∗ ) 2+ ( v∗T 1m ) 2 ] , v = v̄ = 2σ̂|w ∗|√ 2π ( cosϕIm×m + 1−cosϕ m+1 1m1 T m ) v∗ . 2. ϕ = π , v = v̄ = 2σ̂|w ∗|√ 2π ( −Im×m + 2m+11m1 T m ) . 3. ϕ = 0 , |w| = w0 ≡ rσ̂ c0 , v = v0 ≡ 2σ̂|w∗|√ 2π v∗ , where w0 ( or c0 ) is given by λ ( w0 ) = 2 . Note that while the global minimum point is degenerated in scales for w , i.e . if ( w , v ) = ( w0 , v0 ) gives the global minimum , ( w , v ) = ( kw0 , v0 ) for any k > 0 also gives the global minimum , the cRelu STE picks up the point at the scale determined by λ = 2 . 4 EXPERIMENTS To confirm our conjecture in more general case , we have numerically studied the Gaussian mixture input with various mean values . As teacher networks , we have tested tanh-type and sin-type as well as Relu-type . For all the examined setups , cRelu STE behaves like the population gradient , while id/Relu STEs show qualitatively different behaviors as described below . To calculate the population loss and STE gradients , we have generated the mixture Gaussian samples and have taken their average . The population gradient has been obtained by calculating the finite difference of the population loss . We have demonstrated the back propagation with learning rate η = 0.01 given in Algorithm 1 . Shown in Fig . 1 are the results of ten mixture Gaussian input with random mean values for each components of Z . We employed m = 20 , n = 25 , and the tanh-type teacher network . We find that the population gradient and cRelu STE show similar results , while id/Relu STEs are completely different . This indicates cRelu STE is less biased than id/Relu STEs . In the case of id/Relu STEs , at early steps up to around 500 step , |w| decreases around the magnitude of the update quantity , so that it begins to oscillate . Then it escapes the oscillation , and the loss function shows the convergence to a point different from the local minimum achieved by population gradient , while |w| becomes larger and larger due to their scale invariance . Interestingly , the values of the loss function are small compared to the one obtained by population gradient , which implies that id/Relu STEs avoid being trapped in the local solution due to their large bias . However , note that even at that point , the magnitude of w continues to grow and eventually become numerically unstable . 5 SUMMARY We have found that breaking symmetry embedded in the network by STEs enhances the possibility of convergence to the true ( local ) minimum of the loss function . We have demonstrated that if an STE breaks the scale symmetry embedded in the one-hidden-layer network with a binary activation , it is more likely to achieve the local minimum than the one which keeps the symmetry . The discussion can be generalized to the network with more symmetries in a straightforward way . The more symmetries embedded in the network an STE breaks , the more likely it is to converge . To confirm the mechanism , we have studied three STEs , identity STE , Relu STE , and cRelu STE , in a simple misspecified model with Gaussian input . We have found that the back-propagation by the cRelu STE , which breaks the scale symmetry , can converge to the global minimum , while identy/Relu STE can not . Finally we have numerically confirmed the mechanism for the mixture Gaussian model with various teacher network . REFERENCES Ron Banner , Yury Nahshan , Elad Hoffer , and Daniel Soudry . Post-training 4-bit quantization of convolution networks for rapid-deployment . arXiv preprint arXiv:1810.05723 , 2018 . Yoshua Bengio , Nicholas Léonard , and Aaron Courville . Estimating or propagating gradients through stochastic neurons for conditional computation . arXiv preprint arXiv:1308.3432 , 2013 . Yash Bhalgat , Jinwon Lee , Markus Nagel , Tijmen Blankevoort , and Nojun Kwak . Lsq+ : Improving low-bit quantization through learnable offsets and better initialization . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops , pp . 696–697 , 2020 . Pengyu Cheng , Chang Liu , Chunyuan Li , Dinghan Shen , Ricardo Henao , and Lawrence Carin . Straight-through estimator as projected wasserstein gradient flow . arXiv preprint arXiv:1910.02176 , 2019 . Jungwook Choi , Zhuo Wang , Swagath Venkataramani , Pierce I-Jen Chuang , Vijayalakshmi Srinivasan , and Kailash Gopalakrishnan . Pact : Parameterized clipping activation for quantized neural networks . arXiv preprint arXiv:1805.06085 , 2018 . Yoni Choukroun , Eli Kravchik , Fan Yang , and Pavel Kisilev . Low-bit quantization of neural networks for efficient inference . In ICCV Workshops , pp . 3009–3018 , 2019 . Simon Du , Jason Lee , Yuandong Tian , Aarti Singh , and Barnabas Poczos . Gradient descent learns one-hidden-layer cnn : Don ’ t be afraid of spurious local minima . In International Conference on Machine Learning , pp . 1339–1348 . PMLR , 2018 . Steven K Esser , Jeffrey L McKinstry , Deepika Bablani , Rathinakumar Appuswamy , and Dharmendra S Modha . Learned step size quantization . arXiv preprint arXiv:1902.08153 , 2019 . Geoffrey Hinton , Nitsh Srivastava , and Kevin Swersky . Neural networks for machine learning . Coursera , video lectures , 264 ( 1 ) :2146–2153 , 2012 . Itay Hubara , Matthieu Courbariaux , Daniel Soudry , Ran El-Yaniv , and Yoshua Bengio . Quantized neural networks : Training neural networks with low precision weights and activations . The Journal of Machine Learning Research , 18 ( 1 ) :6869–6898 , 2017 . Raghuraman Krishnamoorthi . Quantizing deep convolutional networks for efficient inference : A whitepaper . arXiv preprint arXiv:1806.08342 , 2018 . Vladimir Kryzhanovskiy , Gleb Balitskiy , Nikolay Kozyrskiy , and Aleksandr Zuruev . Qpp : Realtime quantization parameter prediction for deep neural networks . In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp . 10684–10692 , 2021 . Daniel Kunin , Javier Sagastuy-Brena , Surya Ganguli , Daniel LK Yamins , and Hidenori Tanaka . Neural mechanics : Symmetry and broken conservation laws in deep learning dynamics . arXiv preprint arXiv:2012.04728 , 2020 . Ziang Long , Penghang Yin , and Jack Xin . Learning quantized neural nets by coarse gradient method for nonlinear classification . Research in the Mathematical Sciences , 8 ( 3 ) :1–19 , 2021 . Mohammad Rastegari , Vicente Ordonez , Joseph Redmon , and Ali Farhadi . Xnor-net : Imagenet classification using binary convolutional neural networks . In European conference on computer vision , pp . 525–542 . Springer , 2016 . Alexander Shekhovtsov and Viktor Yanush . Reintroducing straight-through estimators as principled methods for stochastic binary networks . arXiv preprint arXiv:2006.06880 , 2020 . Penghang Yin , Jiancheng Lyu , Shuai Zhang , Stanley Osher , Yingyong Qi , and Jack Xin . Understanding straight-through estimator in training activation quantized neural nets . arXiv preprint arXiv:1903.05662 , 2019 . Ritchie Zhao , Yuwei Hu , Jordan Dotzel , Chris De Sa , and Zhiru Zhang . Improving neural network quantization without retraining using outlier channel splitting . In International conference on machine learning , pp . 7543–7552 . PMLR , 2019 . Shuchang Zhou , Yuxin Wu , Zekun Ni , Xinyu Zhou , He Wen , and Yuheng Zou . Dorefa-net : Training low bitwidth convolutional neural networks with low bitwidth gradients . arXiv preprint arXiv:1606.06160 , 2016 . A DERIVATION OF SEC.3 In the Appendix , we provide the results of the Gaussian with variance 1 , N ( 0 , 1 ) . For some function f ( Z ) , the expectation value of the Gaussian with the generic variance σ̂ , N ( 0 , σ̂2 ) , shown in Sec.3 is written as EZ∼N ( 0 , σ2 ) [ f ( Z ) ] = √ 1 2πσ̂2 ∫ ∏ i , j dZijf ( Z ) e − ∑ i , j Z 2 ij 2σ̂2 = √ 1 2π ∫ ∏ i , j dZ ′ijf ( σ̂Z ′ ) e− ∑ i , j Z ′2 ij 2 = EZ∼N ( 0,1 ) [ f ( σ̂Z ) ] ( 32 ) where we have changed the integral variables from Z to Z ′ = Z/σ . Therefore we get the results with the variance σ̂ by only changing from f ( Z ) to f ( σ̂Z ) . A.1 DERIVATION OF LOSS FUNCTION The expectaion of the loss is divided into the following three terms : L ( v , w ; v∗ , w∗ ) = 1 2 EZ [ vTσ ( Zw ) vTσ ( Zw ) ] − EZ [ vTσ ( Zw ) v∗T fRelu ( Zw ∗ ) ] + 1 2 EZ [ v∗T fRelu ( Zw ∗ ) v∗T fRelu ( Zw ∗ ) ] ( 33 ) The calculation of the first term and the third term are given in “ Proof of Lemma 1 ” in Yin et al . ( 2019 ) and “ Proof of Section 3 ” in Ref . Du et al . ( 2018 ) , respectively : 1 2 EZ [ vTσ ( Zw ) vTσ ( Zw ) ] + 1 2 EZ [ v∗T fRelu ( Zw ∗ ) v∗T fRelu ( Zw ∗ ) ] = 1 8 vT ( Im×m + 1m1 T m ) v + 1 4π ( w∗ ) 2v∗T ( ( π − 1 ) Im×m + 1m1Tm ) v∗ ( 34 ) To discuss the second term , we evaluate the following quantity : Fij = EZ [ σ ( Zw ) fRelu ( Zw∗ ) ] ij = ( 1√ 2π ) mn ∫ ∏ k , l dZkl e− 12 ∑k , l Z2klσ ( ∑ m′ Zim′wm′ ) ( ∑ m′ Zjm′w ∗ m′ ) σ ( ∑ m′ Zjm′w ∗ m′ ) ( 35 ) ( i ) i 6= j case : Fij = ( 1√ 2π ) 2n ∫ ( ∏ l dZildZjl ) e− 1 2 ∑ l ( Z 2 il+Z 2 jl ) σ ( ∑ m′ Zim′wm′ ) ( ∑ m′ Zjm′w ∗ m′ ) σ ( ∑ m′ Zjm′w ∗ m′ ) = ( 1√ 2π ) 2n ∫ ( ∏ l dZ̃ildZ̃jl ) e− 1 2 ∑ l ( Z̃ 2 il+Z̃ 2 jl ) σ ( Z̃i1|w| ) ( Z̃j1|w∗| ) σ ( Z̃j1|w∗| ) = ( 1√ 2π ) 2 ∫ dZ̃i1dZ̃j1e − 12 ( Z̃ 2 i1+Z̃ 2 j1 ) σ ( Z̃i1 ) ( Z̃j1|w∗| ) σ ( Z̃j1 ) = 1 2 √ 2π |w∗| , ( 36 ) where we used the orthogonal transformation in the second line : Z̃il = ∑ m′ Oilm′Zim′ , Z̃jl = ∑ m′ Ojlm′Zjm′ ( 37 ) with Oi1m′ = wm′/|w| and O j 1m′ = w ∗ m′/|w∗| . ( ii ) i = j case : Fii = ( 1√ 2π ) n ∫ ∏ l dZile − 12 ∑ l Z 2 ilσ ( ∑ m Zimwm ) ( ∑ m Zimw ∗ m ) σ ( ∑ m Zimw ∗ m ) . ( 38 ) Without loss of generality , we can choose w∗ = ( w∗,0n−1 ) , w = ( w1 , w2,0n−2 ) ( 39 ) with w∗ > 0 , so that this can be written as Fii = ( 1√ 2π ) 2 ∫ dZi1dZi2e − 12 ( Z 2 i1+Z 2 i2 ) σ ( 2∑ m=1 Zimwm ) Zi1w ∗σ ( Zi1 ) = ( 1√ 2π ) 2 ∫ dZdθZe− 1 2Z 2 σ ( cos ( ϕ− θ ) ) Z cos θw∗σ ( cos θ ) = 1 2π ∫ ∞ 0 dZ ∫ π/2 ϕ−π/2 dθZ2e− 1 2Z 2 cos θw∗ = 1 2 √ 2π w∗ ( 1 + cosϕ ) ( 40 ) where ϕ is the angle between w∗ and w . Eqs . ( 36 ) and ( 40 ) are combined as Fij = 1 2 √ 2π |w∗| ( 1 + cosϕ ) δij + 1 2 √ 2π |w∗| ( 1− δij ) , ( 41 ) so that the second term in Eq . ( 33 ) is expressed as EZ [ vTσ ( Zw ) v∗T fRelu ( Zw ∗ ) ] = |w∗| 2 √ 2π vT ( cosϕIm×m + 1m1 T m ) v∗ . ( 42 ) Consequently , the loss function is summarized as L ( v , w ; v∗ , w∗ ) = 1 8 vT ( Im×m + 1m1 T m ) v − |w ∗| 2 √ 2π vT ( cosϕIm×m + 1m1 T m ) v∗ + 1 4π |w∗|2v∗T ( ( π − 1 ) Im×m + 1m1Tm ) v∗ . ( 43 ) The population gradient w.r.t v and w is thus given by ∂L ∂v = 1 4 ( Im×m + 1m1 T m ) v − σ̂|w ∗| 2 √ 2π ( cosϕIm×m + 1m1 T m ) v∗ , ( 44 ) ∂L ∂w = 1 |w| ∂L ∂ϕ eϕ = σ̂|w∗| 2 √ 2π|w| sinϕ ( vTv∗ ) eϕ , ( 45 ) where without loss of generality , we can take ew = ( cosϕ , sinϕ,0n−2 ) , eϕ = ( − sinϕ , cosϕ,0n−2 ) . Note that ew · ∂L∂w = 0 is satisfied due to the scale symmetry for the loss . A.2 CLASSIFICATION OF STATIONARY POINTS Using Eq . ( 21 ) , the loss at the three stationary points is rewritten as L = −|w ∗|2 4π v∗T ( cos2 ϕIm×m + ( − ( cosϕ− 1 ) 2 m+ 1 + 1 ) 1m1 T m ) v∗ + |w∗|2 2π v∗T ( ( π − 1 ) Im×m + 1m1Tm ) v∗ . ( 46 ) Obviously , this shows that ϕ = 0 is the global minimum point , L ≥ L|ϕ=0 = |w∗|2 4π ( π − 2 ) v∗Tv . ( 47 ) At ϕ = π , this becomes L|ϕ=π = − |w∗|2 4π v∗T ( Im×m + ( −4 m+ 1 + 1 ) 1m1 T m ) v∗ + |w∗|2 4π v∗T ( ( π − 1 ) Im×m + 1m1Tm ) v∗ = |w∗|2 4π v∗T ( ( π − 2 ) Im×m + 4 m+ 1 1m1 T m ) v∗ ( 48 ) At ϕ = ϕ̄ = arccos [ ( v∗T 1m ) 2 − ( m+1 ) ( v∗ ) 2+ ( v∗T 1m ) 2 ] , this becomes L|ϕ=ϕ̄ = − |w∗|2 4π v∗T { ( v∗T1m ) 2 − ( m+ 1 ) ( v∗ ) 2 + ( v∗T1m ) 2 } 2 Im×m + − ( m+ 1 ) { ( v∗ ) 2 − ( m+ 1 ) ( v∗ ) 2 + ( v∗T1m ) 2 } 2 + 1 1m1Tm v∗ + |w∗|2 4π v∗T ( ( π − 1 ) Im×m + 1m1Tm ) v∗ = −|w ∗|2 4π { ( v∗T1m ) 2 ( v∗ ) 2 − ( m+ 1 ) ( v∗ ) 2 + ( v∗T1m ) 2 + ( v∗T1m ) 2 } + |w∗|2 4π v∗T ( ( π − 1 ) Im×m + 1m1Tm ) v∗ . ( 49 ) Using these results , we obtain L|ϕ=ϕ̄ − L|ϕ=π = −|w ∗|2 4π ( v∗T1m ) 2 ( v∗ ) 2 − ( m+ 1 ) ( v∗ ) 2 + ( v∗T1m ) 2 + |w∗|2 4π ( v∗ ) 2 − |w ∗|2 4π ( v∗T1m ) 2 4 m+ 1 = |w∗|2 4π { ( m+ 1 ) ( v∗ ) 2 − 2 ( v∗T1m ) 2 } 2 { ( m+ 1 ) ( v∗ ) 2 − ( v∗T1m ) 2 } ( m+ 1 ) . ( 50 ) Note that because ( m + 1 ) ( v∗ ) 2 ≥ 2 ( v∗T1m ) 2 is satisfied to exist the stationary point at ϕ = ϕ̄ , the loss at ϕ = π is smaller than the one at at ϕ = ϕ̄ , L|v=v̄ , ϕ=ϕ̄ ≥ L|v=vπ , ϕ=π . ( 51 ) is satisfied . When ( m + 1 ) ( v∗ ) 2 = 2 ( v∗T1m ) 2 , L|ϕ=ϕ̄ = L|ϕ=π , because the middle stationary point at ϕ = ϕ̄ is merged into the point at ϕ = π . This implies that ϕ = 0 is the global minimum , ϕ = π is the local minimum , and ϕ = ϕ̄ is the saddle point or the local maximum if ( m+ 1 ) ( v∗ ) 2 ≥ 2 ( v∗T1m ) 2 . To fully clarify whether the stationary points are local minimum , maximum or saddle point , we have calculated the hessian matrix , H = ( ∂2L ∂v2 ∂2L ∂v∂ϕ ∂2L ∂ϕ∂v ∂2L ∂ϕ2 ) = ( 1 4 ( Im×m + 1m1 T m ) |w∗| 2 √ 2π sinϕv∗ |w∗| 2 √ 2π sinϕv∗T |w ∗| 2 √ 2π vTv∗ cosϕ ) . ( 52 ) ( i ) v = v̄ , ϕ = ϕ̄ : For z = ( x , y ) T ( x ∈ Rm , y ∈ R ) , zTHz = 1 4 xT ( Im×m + 1m1 T m ) x + |w∗|√ 2π y sin ϕ̄ ( xTv∗ ) = 1 4 ( xT1m ) 2 + ( 1 2 x + |w∗|y sin ϕ̄√ 2π v∗ ) 2 − |w ∗|2y2 sin2 ϕ̄ ( v∗ ) 2 2π , ( 53 ) where we used v̄Tv∗ = 0 . Therefore , depending on x and y , the Hessian can be either positive or negative , which means the stationary point is a saddle point . ( ii ) v = vπ ≡ 2|w ∗|√ 2π ( −Im×m + 2m+11m1 T m ) v∗ , ϕ = π H = ( 1 4 ( Im×m + 1m1 T m ) 0 0 − |w ∗| 2 √ 2π vTπ v ∗ ) . ( 54 ) Note that vTπ v ∗ = 2|w ∗|√ 2π v∗T ( −Im×m + 2m+11m1 T m ) v∗ . If ( m + 1 ) ( v∗ ) 2 ≥ 2 ( v∗T1m ) 2 , then vTπ v ∗ ≤ 0 , so that the matrix becomes a positive definite matrix : For any x ∈ Rm , y ∈ R , this satisfies 1 4 xT ( Im×m + 1m1 T m ) x− |w ∗| 2 √ 2π vTπ v ∗y2 ≥ 0 , ( 55 ) which means the stationary point is the local minimum . On the other hand , if ( m + 1 ) ( v∗ ) 2 ≤ 2 ( v∗T1m ) 2 , the matrix becomes either positive or negative , depending on x and y , so that this stationary point becomes a saddle point . ( iii ) v = v0 ≡ 2|w ∗|√ 2π v∗ , ϕ = 0 H = ( 1 4 ( Im×m + 1m1 T m ) 0 0 |w ∗| 2 √ 2π vT0 v ∗ ) . ( 56 ) The Hessian becomes a positive definite matrix : For any x ∈ Rm , y ∈ R , this satisfies 1 4 xT ( Im×m + 1m1 T m ) x + |w∗| 2 √ 2π vT0 v ∗y2 ≥ 0 . ( 57 ) A.3 DERIVATION OF IDENTITY STE GRADIENT By substituting µ′ ( x ) = 1 into Eq . ( 23 ) , we obtain EZ [ g ] i = ( 1√ 2π ) mn ∫ ∏ k , l dZkl e− 12 ∑k , l Z2kl ∑ j Zjivj × ( ∑ k vkσ ( ∑ l Zklwl ) − ∑ k v∗kfRelu ( ∑ l Zklw ∗ l ) ) . ( 58 ) We calculate each term as follows . A.3.1 CALCULATION OF THE FIRST TERM We calculate the first term as was done in Lemma 9 ( and also Lemma 11 ) in Yin et al . ( 2019 ) . To discuss the first term , we evaluate F idijk ≡ EZ [ Zji [ σ ( Zw ) ] k ] . ( 59 ) Using F idijk , the first term can be expressed as first term = ∑ j , k F idijkvjvk . ( 60 ) ( i ) j 6= k case : F idijk = 0 , ( 61 ) because of the odd symmetry for i-th and j-th components in the integrand . ( ii ) j = k case : F idijj = ( 1√ 2π ) n ∫ ( ∏ l dZjl ) e− 1 2 ∑ l Z 2 jlZjiσ ( ∑ l Zjlwl ) = ( 1√ 2π ) n ∫ ( ∏ l dZ̃jl ) e− 1 2 ∑ l Z̃ 2 jl ( ∑ m′ Oj Tim′Z̃jm′ ) σ ( Z̃j1 ) = ( 1√ 2π ) n ∫ ( ∏ l dZ̃jl ) e− 1 2 ∑ l Z̃ 2 jlOj Ti1 Z̃j1σ ( Z̃j1 ) = ( 1√ 2π ) ∫ dZ̃j1e − 12 Z̃ 2 j1Oj Ti1 Z̃j1σ ( Z̃j1 ) = ( 1√ 2π ) Oj Ti1 = ( 1√ 2π ) Oj1i = 1√ 2π wi |w| ( 62 ) where we used the orthogonal transformation in the second line : Z̃jl = ∑ m′ Ojlm′Zjm′ ( 63 ) with Oi1m′ = wm′/|w| . The first term is reduced to first term = 1√ 2π wi |w| ( vTv ) . ( 64 ) A.3.2 CALCULATION OF THE SECOND TERM To discuss the second term , we evaluate Gidijk = EZ [ Zji [ fRelu ( Zw∗ ) ] k ] . ( 65 ) Using Gidijk , the second term can be expressed as second term = ∑ j , k Gidijkvjv ∗ k. ( 66 ) ( i ) j 6= k case : Gidijk = 0 , ( 67 ) because of the odd symmetry . ( ii ) j = k case : Gidijj = ( 1√ 2π ) n ∫ ( ∏ l dZjl ) e− 1 2 ∑ l Z 2 jlZjifRelu ( ∑ l Zjlw ∗ l ) = ( 1√ 2π ) n ∫ ( ∏ l dZ̃jl ) e− 1 2 ∑ l Z̃ 2 jl ( ∑ m′ Oj Tim′Z̃jm′ ) fRelu ( |w∗|Z̃j1 ) = ( 1√ 2π ) n ∫ ( ∏ l dZ̃jl ) e− 1 2 ∑ l Z̃ 2 jlOj Ti1 Z̃j1fRelu ( |w∗|Z̃j1 ) = ( 1√ 2π ) ∫ dZ̃j1e − 12 Z̃ 2 j1Oj Ti1 Z̃j1fRelu ( |w∗|Z̃j1 ) = ( 1√ 2π ) ∫ dZ̃j1e − 12 Z̃ 2 j1Oj Ti1 Z̃ 2 j1|w∗|σ ( |w∗|Z̃j1 ) = |w∗| 2 Oj Ti1 = |w∗| 2 Oj1i = 1 2 w∗i ( 68 ) where we have used the orthogonal transformation in the second line : Z̃jl = ∑ m′ Ojlm′Zjm′ ( 69 ) with Oi1m′ = w∗m′/|w∗| , and also have used∫ ∞ 0 dxx2e− ( 1/2 ) x 2 = √ 2π 2 . ( 70 ) The second term is written as second term = 1 2 wi ( vTv∗ ) . ( 71 ) A.4 DERIVATION OF RELU STE GRADIENT By substituting µ′ ( x ) = σ ( x ) into Eq . ( 23 ) , we obtain EZ [ grelu ] i = ( 1√ 2π ) mn ∫ ∏ k , l dZkl e− 12 ∑k , l Z2kl ∑ j Zjivjσ ( ∑ l Zjlwl ) × ( ∑ k vkσ ( ∑ l Zklwl ) − ∑ k v∗kfRelu ( ∑ l Zklw ∗ l ) ) . ( 72 ) We calculate each term as follows . A.4.1 CALCULATION OF THE FIRST TERM We calculate the first term as was done in Lemma9 ( and also Lemma11 ) in Yin et al . ( 2019 ) . We evaluate F reluijk = EZ [ Zji [ σ ( Zw ) ] j [ σ ( Zw ) ] k ] . ( 73 ) Using F reluijk , the first term can be expressed as first term = ∑ j , k F reluijk vjvk . ( 74 ) ( i ) j 6= k case : F reluijk = ( 1√ 2π ) 2n ∫ ( ∏ l dZjldZkl ) e− 1 2 ∑ l Z 2 jl+Z 2 klZjiσ ( ∑ l Zjlwl ) σ ( ∑ l Zklwl ) = ( 1√ 2π ) 2n ∫ ( ∏ l dZ̃jldZ̃kl ) e− 1 2 ∑ l Z̃ 2 jl+Z̃ 2 kl ( ∑ m′ Oj Tim′Z̃jm′ ) σ ( Z̃j1 ) σ ( Z̃k1 ) = ( 1√ 2π ) 2 ∫ dZ̃j1dZ̃k1e − 12 ( Z̃ 2 j1+Z̃ 2 k1 ) Oj Ti1 Z̃j1σ ( Z̃j1 ) σ ( Z̃k1 ) = 1 2 √ 2π Oj Ti1 = 1 2 √ 2π wi |w| , ( 75 ) where we used the orthogonal transformation in the second line : Z̃jl = ∑ m′ Ojlm′Zjm′ ( 76 ) with Oi1m′ = wm′/|w| . ( ii ) j = k case : We get the same expression given in Eq . ( 62 ) , F reluijj = ( 1√ 2π ) n ∫ ( ∏ l dZjl ) e− 1 2 ∑ l Z 2 jlZjiσ ( ∑ l Zjlwl ) = 1√ 2π wi |w| . ( 77 ) The first term is written as first term = 1 2 √ 2π wi |w| vT ( Im×m + 1m1 T m ) v. ( 78 ) A.4.2 CALCULATION OF SECOND TERM To discuss the second term , we evaluate Greluijk = EZ [ Zji [ [ σ ( Zw ) ] j fRelu ( Zw ∗ ) ] k ] . ( 79 ) Using Greluijk , the second term can be expressed as second term = ∑ j , k Greluijk vjv ∗ k. ( 80 ) ( i ) j 6= k case : Greluijk = ( 1√ 2π ) 2n ∫ ( ∏ l dZjldZkl ) e− 1 2 ∑ l Z 2 jl+Z 2 klZjiσ ( ∑ l Zjlwl ) fRelu ( ∑ l Zklw ∗ l ) = ( 1√ 2π ) 2n ∫ ( ∏ l dZ̃jldZ̃kl ) e− 1 2 ∑ l Z̃ 2 jl+Z̃ 2 kl ( ∑ m′ Oj Tim′Z̃jm′ ) σ ( Z̃j1 ) fRelu ( |w∗|Z̃k1 ) = ( 1√ 2π ) 2 ∫ dZ̃j1dZ̃k1e − 12 ( Z̃ 2 j1+Z̃ 2 k1 ) Oj Ti1 Z̃j1σ ( Z̃j1 ) |w∗|Z̃k1σ ( Z̃k1 ) = |w∗| 2π Oj Ti1 = 1 2π |w∗| |w| wi , ( 81 ) where we used the orthogonal transformation in the second line : Z̃jl = ∑ m′ Ojlm′Zjm′ , Z̃kl = ∑ m′ Oklm′Zkm′ ( 82 ) with Oi1m′ = wm′/|w| and O j 1m′ = w ∗ m′/|w∗| . ( ii ) j = k case : Greluijj = ( 1√ 2π ) m ∫ ( ∏ l dZjl ) e− 1 2 ∑ l Z 2 jlZjiσ ( ∑ l Zjlwl ) fRelu ( ∑ l Zjlw ∗ l ) = ( 1√ 2π ) m ∫ ( ∏ l dZjl ) e− 1 2 ∑ l Z 2 jlZjiσ ( 2∑ l=1 Zjlwl ) fRelu ( w ∗Zj1 ) = 1 2π ∫ ( 2∏ l=1 dZjl ) e− 1 2 ∑2 l=1 Z 2 jlZji ( δi1 + δi2 ) σ ( 2∑ l=1 Zjlwl ) fRelu ( w ∗Zj1 ) = 1 2π ∫ dZdθZe− 1 2Z 2 ( Z cos θδi1 + Z sin θδi2 ) σ ( Zw cos ( θ − ϕ ) ) fRelu ( w∗Z cos θ ) = 1 2π ∫ dZdθZe− 1 2Z 2 ( Z cos θδi1 + Z sin θδi2 ) σ ( Zw cos ( θ − ϕ ) ) w∗Z cos θσ ( cos θ ) ( 83 ) = w∗ 2π ∫ ∞ 0 dZZ3e− 1 2Z 2 ∫ π/2 −π/2+ϕ dθ ( cos2 θδi1 + cos θ sin θδi2 ) = w∗ π ( ( sin 2ϕ 4 + 1 2 ( π − ϕ ) ) δi1 + 1− cos 2ϕ 4 δi2 ) = w∗ π ( 1 2 ( π − ϕ ) δi1 + sinϕ 2 ( cosϕδi1 + sinϕδi2 ) ) ( 84 ) where we have chosen w∗ = ( w∗,0n−1 ) , w = ( w1 , w2,0n−2 ) ( 85 ) with w∗ > 0 and ϕ is defined as the angle between w∗ and w. We also have used the formula , ∫ ∞ 0 dxx3e− ( 1/2 ) x 2 = 2 , ∫ dθ cos2 θ = sin 2θ 4 + 1 2 θ , ∫ dθ cos θ sin θ = −1 4 cos 2θ . ( 86 ) Note that w∗i |w∗| = δi1 , wi |w| = cosϕδi1 + sinϕδi2 . ( 87 ) This reduces to Greluijj = 1 π ( 1 2 ( π − ϕ ) w∗i + sinϕ 2 |w∗| |w| wi ) ( 88 ) In summary , the second term is written as second term = 1 2π |w∗| |w| wi [ vT ( −Im×m + 1m1Tm ) v∗ ] + ( ( π − ϕ ) w∗i + |w∗| sinϕ |w| wi ) ( vTv∗ ) 2π . ( 89 ) A.5 DERIVATION OF CLIPPED RELU STE GRADIENT By substituting µ′ ( x ) = σ ( x ) σ ( r − x ) into Eq . ( 23 ) , we obtain EZ [ gcrelu ] i = ( 1√ 2π ) mn ∫ ∏ k , l dZkl e− 12 ∑k , l Z2kl × ∑ j Zjivjσ ( ∑ l Zjlwl ) σ ( r − ∑ l Zjlwl ) × ( ∑ k vkσ ( ∑ l Zklwl ) − ∑ k v∗kfRelu ( ∑ l Zklw ∗ l ) ) . ( 90 ) We calculate each term as follows . A.5.1 CALCULATION OF THE FIRST TERM To discuss the first term , we evaluate F creluijk = EZ [ Zji [ σ ( Zw ) σ ( r −Zw ) ] j [ σ ( Zw ) ] k ] . ( 91 ) Using F creluijk , the first term can be expressed as first term = ∑ j , k F creluijk vjvk . ( 92 ) ( i ) j 6= k case : F creluijk = ( 1√ 2π ) 2n ∫ ( ∏ l dZjldZkl ) e− 1 2 ∑ l Z 2 jl+Z 2 klZji × σ ( ∑ l Zjlwl ) σ ( r − ∑ l Zjlwl ) σ ( ∑ l Zklwl ) = ( 1√ 2π ) 2n ∫ ( ∏ l dZ̃jldZ̃kl ) e− 1 2 ∑ l Z̃ 2 jl+Z̃ 2 kl ( ∑ m′ Oj Tim′Z̃jm′ ) × σ ( Z̃j1 ) σ ( r − |w|Z̃j1 ) σ ( Z̃k1 ) = ( 1√ 2π ) 2 ∫ dZ̃j1dZ̃k1e − 12 ( Z̃ 2 j1+Z̃ 2 k1 ) Oj Ti1 Z̃j1σ ( Z̃j1 ) σ ( r − |w|Z̃j1 ) σ ( Z̃k1 ) = 1 2 √ 2π ∫ r/|w| 0 dZ̃j1e − 12 Z̃ 2 j1Oj Ti1 Z̃j1 = 1 2 √ 2π Oj Ti1 ( 1− e− 1 2 ( r |w| ) 2 ) = 1 2 √ 2π ( 1− e− 1 2 ( r |w| ) 2 ) wi |w| , ( 93 ) where we used the orthogonal transformation in the second line : Z̃jl = ∑ m′ Ojlm′Zjm′ ( 94 ) with Oi1m′ = wm′/|w| . ( ii ) j = k case : F creluijj = ( 1√ 2π ) n ∫ ( ∏ l dZjl ) e− 1 2 ∑ l Z 2 jlZjiσ ( ∑ l Zjlwl ) σ ( r − ∑ l Zjlwl ) = ( 1√ 2π ) n ∫ ( ∏ l dZ̃jl ) e− 1 2 ∑ l Z̃ 2 jl ( ∑ m′ Oj Tim′Z̃jm′ ) σ ( Z̃j1 ) σ ( r − |w|Z̃j1 ) = 1√ 2π ∫ dZ̃j1e − 12 Z̃ 2 j1Oj Ti1 Z̃j1σ ( Z̃j1 ) σ ( r − |w|Z̃j1 ) = 1√ 2π ∫ r/|w| 0 dZ̃j1e − 12 Z̃ 2 j1Oj Ti1 Z̃j1 = 1√ 2π Oj Ti1 ( 1− e− 1 2 ( r |w| ) 2 ) = 1√ 2π ( 1− e− 1 2 ( r |w| ) 2 ) wi |w| , ( 95 ) In summary , the first term is written as first term = 1 2 √ 2π wi |w| ( 1− e− 1 2 ( r |w| ) 2 ) vT ( Im×m + 1m1 T m ) v. ( 96 ) A.5.2 CALCULATION OF THE SECOND TERM To discuss the second term , we evaluate Gcreluijk = EZ [ Zji [ [ σ ( Zw ) σ ( r −Zw ) ] j fRelu ( Zw ∗ ) ] k ] . ( 97 ) Using Gcreluijk , the second term can be expressed as second term = ∑ j , k Gcreluijk vjv ∗ k. ( 98 ) ( i ) j 6= k case : Gcreluijk = ( 1√ 2π ) 2n ∫ ( ∏ l dZjldZkl ) e− 1 2 ∑ l Z 2 jl+Z 2 klZji × σ ( ∑ l Zjlwl ) σ ( r − ∑ l Zjlwl ) fRelu ( ∑ l Zklw ∗ l ) = ( 1√ 2π ) 2n ∫ ( ∏ l dZ̃jldZ̃kl ) e− 1 2 ∑ l Z̃ 2 jl+Z̃ 2 kl ( ∑ m′ Oj Tim′Z̃jm′ ) × σ ( Z̃j1 ) σ ( r − |w|Z̃j1 ) fRelu ( |w∗|Z̃k1 ) = ( 1√ 2π ) 2 ∫ dZ̃j1dZ̃k1e − 12 ( Z̃ 2 j1+Z̃ 2 k1 ) Oj Ti1 Z̃j1 × σ ( Z̃j1 ) σ ( r − |w|Z̃j1 ) |w∗|Z̃k1σ ( Z̃k1 ) = ( 1√ 2π ) 2 ∫ dZ̃j1e − 12 Z̃ 2 j1Oj Ti1 Z̃j1σ ( Z̃j1 ) σ ( r − |w|Z̃j1 ) |w∗| = ( 1√ 2π ) 2 ∫ r/|w| 0 dZ̃j1e − 12 Z̃ 2 j1Oj Ti1 Z̃j1|w ∗| = |w∗| 2π Oj Ti1 ( 1− e− 1 2 ( r |w| ) 2 ) = |w∗| 2π ( 1− e− 1 2 ( r |w| ) 2 ) wi |w| , ( 99 ) where we used the orthogonal transformation in the second line : Z̃il = ∑ m′ Oilm′Zim′ , Z̃kl = ∑ m′ Oklm′Zkm′ ( 100 ) with Oi1m′ = wm′/|w| and O j 1m′ = w ∗ m′/|w∗| . ( ii ) j = k case : Gcreluijj = ( 1√ 2π ) m ∫ ( ∏ l dZjl ) e− 1 2 ∑ l Z 2 jlZji × σ ( ∑ l Zjlwl ) σ ( r − ∑ l Zjlwl ) fRelu ( ∑ l Zjlw ∗ l ) = ( 1√ 2π ) m ∫ ( ∏ l dZjl ) e− 1 2 ∑ l Z 2 jlZji × σ ( 2∑ l=1 Zjlwl ) σ ( r − 2∑ l=1 Zjlwl ) fRelu ( w ∗Zj1 ) = 1 2π ∫ ( 2∏ l=1 dZjl ) e− 1 2 ∑2 l=1 Z 2 jlZji ( δi1 + δi2 ) × σ ( 2∑ l=1 Zjlwl ) σ ( r − 2∑ l=1 Zjlwl ) fRelu ( w ∗Zj1 ) = 1 2π ∫ dZdθZe− 1 2Z 2 ( Z cos θδi1 + Z sin θδi2 ) × σ ( Zw cos ( θ − ϕ ) ) σ ( r − 2∑ l=1 Zjlwl ) fRelu ( w ∗Z cos θ ) = 1 2π ∫ dZdθZe− 1 2Z 2 ( Z cos θδi1 + Z sin θδi2 ) × σ ( Zw cos ( θ − ϕ ) ) σ ( r − Zw cos ( θ − ϕ ) ) w∗Z cos θσ ( cos θ ) ( 101 ) = w∗ 2π ∫ π/2 −π/2+ϕ dθ ( cos2 θδi1 + cos θ sin θδi2 ) ∫ rw cos ( θ−ϕ ) 0 dZZ3e− 1 2Z 2 = w∗ 2π ∫ π/2 −π/2+ϕ dθ ( cos2 θδi1 + cos θ sin θδi2 ) × { 2− ( ( r w cos ( θ − ϕ ) ) 2 + 2 ) e− 1 2 ( r w cos ( θ−ϕ ) ) 2 } ( 102 ) where we have chosen w∗ = ( w∗,0n−1 ) , w = ( w1 , w2,0n−2 ) ( 103 ) with w∗ > 0 and ϕ is defined as the angle between w∗ and w. We also have used the formula , ∫ t 0 dxx3e− 1 2x 2 = 2− ( t2 + 2 ) e− 12 t 2 . ( 104 ) Note that w∗i |w∗| = δi1 , wi |w| = cosϕδi1 + sinϕδi2 . ( 105 ) Using C ( w , ϕ ) , S ( w , ϕ ) defined as C ( w , ϕ ) ≡ w ∗ 2π ∫ π/2 −π/2+ϕ dθ cos2 θ { 2− ( ( r w cos ( θ − ϕ ) ) 2 + 2 ) e− 1 2 ( r w cos ( θ−ϕ ) ) 2 } , S ( w , ϕ ) ≡ w ∗ 2π ∫ π/2 −π/2+ϕ dθ sin θ cos θ { 2− ( ( r w cos ( θ − ϕ ) ) 2 + 2 ) e− 1 2 ( r w cos ( θ−ϕ ) ) 2 } , ( 106 ) If ϕ 6= 0 , π , Gcreluijj is formally written as Gcreluijj = C ( w , ϕ ) w∗i |w∗| + S ( w , ϕ ) ( 1 sinϕ wi |w| − cotϕ w ∗ i |w∗| ) ( 107 ) If ϕ = 0 or π , Gcreluijj is formally written as Gcreluijj = C ( w , 0 ) w∗i |w∗| for ϕ = 0 , ( 108 ) Gcreluijj = 0 for ϕ = π ( 109 ) where we used S ( w , 0 ) = S ( w , π ) = C ( w , π ) = 0 and C ( w , 0 ) = w∗ 2π ∫ π/2 −π/2 dθ cos2 θ { 2− ( ( r w cos θ ) 2 + 2 ) e− 1 2 ( r w cos θ ) 2 } = w∗ 2π ( π − r w e− 1 2 ( r w ) 2√ 2π − πerfc ( r√ 2w ) ) ( 110 ) with the following formula∫ π/2 −π/2 dθe− a2 2 cos2 θ = πerfc ( a√ 2 ) , ( 111 ) ∫ π/2 −π/2 dθ cos2 θe− a2 2 cos2 θ = 1 2 ( ae− a2 2 √ 2π + ( 1− a2 ) πerfc ( a√ 2 ) ) . ( 112 ) In summary , the second term is written as second term = 1 2π |w∗| |w| ( 1− e− 1 2 ( r |w| ) 2 ) wi [ vT ( −Im×m + 1m1Tm ) v∗ ] + { C ( w , ϕ ) w∗i |w∗| + S ( w , ϕ ) ( 1 sinϕ wi |w| − cotϕ w ∗ i |w∗| ) } ( vTv∗ ) . ( 113 )

Summary: The paper does a theoretical analysis of various gradient approximations for the non differentiable sign function. They investigate 3 gradient estimators based on the STE assumption, namely (identity) STE, ReLU STE, and clipped STE (cReLU). They show theoretically that a gradient estimator which breaks the scale symmetry (clipped STE) in the network is more likely to achieve a local minimum than one that keeps the symmetry.